Assessment of mechanical behavior of sprayed concrete reinforced with waste tire textile fibers

The primary objective of this research is to assess the mechanical properties of shotcrete or sprayed concrete reinforced with waste tire textile fibers (WTTFs). Shotcrete is extensively employed in civil and mining engineering projects for support systems. This study examines the physical and mechanical characteristics of shotcrete samples, both without fibers and reinforced with WTTFs, including density, water absorption, volume of permeable voids, ultrasonic pulse velocity, uniaxial compressive strength, and splitting tensile strength. The reinforced samples consist of WTTF fibers at 0.5%, 1%, and 1.5% concentrations. Test results demonstrate that the inclusion of waste tire textile fibers enhances the mechanical properties of shotcrete, particularly its deformability and energy absorption capacity. Based on the test outcomes, a 1% fiber concentration is recommended as the most suitable ratio for utilizing waste tire textile fibers in shotcrete. Furthermore, these flexible fibers do not impede shotcrete pumping or spraying capabilities.

High-performance cementitious composites containing nanostructured carbon additives made from charred coal fines

Carbon-based nanomaterials, such as carbon nanoplatelets, graphene oxide, and carbon quantum dots, have many possible end-use applications due to their ability to impart unique mechanical, electrical, thermal, and optical properties to cement composites. Despite this potential, these materials are rarely used in the construction industry due to high material costs and limited data on performance and durability. In this study, domestic coal is used to fabricate low-cost carbon nanomaterials that can be used economically in cement formulations. A range of chemical and physical processing approaches are employed to control the size, morphology, and chemical functionalization of the carbon nanomaterial, which improves its miscibility with cement formulations and its impact on mechanical properties and durability. At loadings of 0.01 to 0.07 wt.% of coal-derived carbon nanomaterial, the compressive and flexural strength of cement samples are enhanced by 24% and 23%, respectively, in comparison to neat cement. At loadings of 0.02 to 0.06 wt.%, the compressive and flexural strength of concrete composites increases by 28% and 21%, respectively, in comparison to neat samples. Additionally, the carbon nanomaterial additives studied in this work reduce cement porosity by 36%, permeability by 86%, and chloride penetration depth by 60%. These results illustrate that low-loadings of coal-derived carbon nanomaterial additives can improve the mechanical properties, durability, and corrosion resistance of cement composites.

Integrated optimization of scheduling for unmanned follow-me cars on airport surface

To promote the application of automated vehicles in large airports, in this study, we present an integrated optimization method for scheduling Unmanned follow-me cars. The scheduling process is divided into three phases: Dispatch, Guidance, and Recycle. For the Dispatch phase, we establish a vehicle assignment model, to allocate the vehicle resource equitably. For the Guidance phase, we offer an quantitative way, to measure the spacing between Unmanned follow-me car and aircraft. To optimize the efficiency of airport operation in the three phases and ensure safety, the collaborative planning model, and the conflict prediction model are established. An improved grey wolf optimization algorithm is adopted to enhance the convergence speed and generalization performance. A case study at Ezhou Huahu Airport in China demonstrates the effectiveness of the methods. The results show that the model of collaborative planning can make the balance of path selection, Unmanned follow-me car's working time, and departure sequence. The convergence speed of the improved algorithm has been increased by 18.75%. The inequity index of vehicle assignment is only 0.015731, and the spatiotemporal distribution of conflicts is influenced by the airport's surface layout.

Transparent ultrahigh-molecular-weight polyethylene/MXene films with efficient UV-absorption for thermal management

The rational use and conversion of energy are the primary means for achieving the goal of carbon neutrality. MXenes can be used for photothermal conversion, but their opaque appearance limits wider applications. Herein, we successfully develop visible-light transparent and UV-absorbing polymer composite film by solution blending the MXene with polyethylene and then vacuum pressing. The resulting film could be quickly heated to 65 °C under 400 mW cm-2 light irradiation and maintained over 85% visible-light transmittance as well as low haze (<12%). The findings of the indoor heat insulation test demonstrate that the temperature of the glass house model covered by this film was 6-7 °C lower than that of the uncovered model, revealing the potential of transparent film in energy-saving applications. In order to mimic the energy-saving condition of the building in various climates, a typical building model with this film as the outer layer of the window was created using the EnergyPlus building energy consumption software. According to predictions, they could reduce yearly refrigeration energy used by 31-61 MJ m-2, and 3%-12% of the total energy used for refrigeration in such structures. This work imply that the film has wide potential for use as transparent devices in energy-related applications.

Effect of borax-modified activator on mechanical properties and drying shrinkage of alkali-activated slag/metakaolin mortar

Alkali-activated materials (AAMs) possess several advantages, such as high strengths and low carbon emissions. However, their application is hindered due to their significant shrinkage. This study explored the effect of borax-modified sodium silicate activator and metakaolin (MK) on the mechanical properties and drying shrinkage (DS) of alkali-activated slag (AAS) and AAS/MK (AASM) mortars. X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy were used to characterize the hydration products. The results showed that the DS reduction of the AAS mortar was related to decreased Na2O content, a reduction in the proportion of mesopores, and the formation of moisture-retaining borate compounds. The DS reduction of the AASM mortar was attributed to the ultra-fine differential effect induced by MK, reducing the connected pores. The modified activator combined with MK increased the chemically bound water content in the matrix. Additionally, the B-O bond and highly active MK improved compactness of the AASM mortar. The use of borax-modified activators and MK provides a new solution to address the significant shrinkage issue in AAMs. This sets the stage for AAMs to potentially replace OPC, contributing to low-carbon emissions and promoting environmental protection.

Shaking table tests of a one-quarter scale model of concrete hollow block masonry houses retrofitted with fiber-reinforced paint

Unreinforced masonry (URM) buildings are prone to significant damage when subjected to ground motion. Some strengthening methods have been proposed to increase the seismic capacity. However, the widespread adoption of these methods faces various challenges, including economic constraints experienced by common people in developing countries, the complexity of implementation, efficiency, and seismic safety of each technique. This paper introduces a new retrofitting method of fiber-reinforced paint using fiberglass as the primary reinforcing material. The advantage of this technique lies in its simplicity and ease of application, with the added benefit of using the paint to improve the appearance of the house. Two 1:4 scale concrete hollow block (CHB) masonry houses were constructed to represent unreinforced masonry and retrofitted masonry structures using fiber-reinforced paint (FR-Paint). The shaking table test results indicate that the retrofitted house model showed improvements of up to 18 times in deformation capacity and up to 13 times in energy dissipation compared to the non-retrofitted house model. FR-Paint has a robust performance even in high input motion at a seismic intensity JMA of 7 (Japan Meteorological Agency). This confirms that this retrofitting method has a high earthquake-resistant performance.

Integrating mangrove growth and failure in coastal flood protection designs

Mangrove forests reduce wave attack along tropical and sub-tropical coastlines, decreasing the wave loads acting on coastal protection structures. Mangrove belts seaward of embankments can therefore lower their required height and decrease their slope protection thickness. Wave reduction by mangroves depends on tree frontal surface area and stability against storms, but both aspects are often oversimplified or neglected in coastal protection designs. Here we present a framework to evaluate how mangrove belts influence embankment designs, including mangrove growth over time and failure by overturning and trunk breakage. This methodology is applied to Sonneratia apetala mangroves seaward of embankments in Bangladesh, considering forest widths between 10 and 1000 m (cross-shore). For water depths of 5 m, wave reduction by mangrove forests narrower than 1 km mostly affects the slope protection and the bank erodibility, whereas the required embankment height is less influenced by mangroves. Sonneratia apetala trees experience a relative maximum in wave attenuation capacity at 10 years age, due to their large submerged canopy area. Once trees are more than 20 years old, their canopy is emergent, and most wave attenuation is caused by trunk and roots. Canopy emergence exposes mangroves to wind loads, which are much larger than wave loads, and can cause tree failure during cyclones. These results stress the importance of including tree surface area and stability models when predicting coastal protection by mangroves.

Mosaic crack mapping of footings by convolutional neural networks

Cracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network (DCNN) to identify cracks in mosaic images composed from UAV photos of concrete footings. UAV images are transformed into 3D footing models, from which the composite images are created. The CNN model is trained on 224 × 224 pixel patches, and training samples are augmented by various image transformation techniques. The proposed method is applied to localize cracks on composite images through the sliding window technique. The proposed VGG16 CNN detection system, with 95% detection accuracy, indicates superior performance to feature-based detection systems.

Developments of a centimeter-level precise muometric wireless navigation system (MuWNS-V) and its first demonstration using directional information from tracking detectors

Various positioning techniques such as Wi-Fi positioning system have been proposed to use in situations where satellite navigation is unavailable. One such system, the muometric positioning system (muPS), was invented for navigation which operates in locations where even radio waves cannot reach such as underwater or underground. muPS takes advantage of a key feature of its probe, cosmic-ray muons, which travel straightforwardly at almost a speed of light in vacuum regardless of the matter they traverse. Similar to other positioning techniques, muPS is a technique to determine the position of a client's muPS receiver within the coordinate defined by reference detectors. This can be achieved either by using time-of-flight (ToF) or angle of arrival (AoA) measurements. The latter configuration (AoA), called the Vector-muPS has recently been invented and the present paper describes the developments of the first prototype of a vector muometric wireless navigation system (MuWNS-V) with this new vector-muPS concept and its demonstration. With MuWNS-V, the reference tracker and the receiver ran wirelessly with fully independent readout systems, and a positioning accuracy of 3.9 cm (RMS) has been achieved. We also evaluated the outcome of measuring continuous indoor localization of a moving receiver with this prototype. Our results indicated that further improvements in positioning accuracy will be attainable by acquiring higher angular resolution of the reference trackers. It is anticipated that "sub-cm level" navigation will be possible for muPS which could be applied to many situations such as future autonomous mobile robot operations.

Effects of the addition of short straight steel fibers on the strength and strains of high-strength concrete during compression

The article presents the effect of the addition of short straight steel fibers on the behavior of high-strength concrete (HSC) under compression (σ-ε curves). Deformations of cylindrical samples were measured simultaneously with the use of linear variable differential transformers (LVDT), strain gauges and the method of digital image correlation (DIC). The study showed that as the content of short straight steel fibers increases, both the composite compressive strength (fc) and strains (ε0), which correspond to the stress equal to the compressive strength, increase as well. To a lesser extent, the effect of short straight fibers on the descending part of the σ-ε curve was observed. An increase in the density and toughness ratio of the compressive strength of high-strength concrete with fibers compared to concrete without fibers was also observed. Moreover, compressive strength of the composite was estimated using the ultrasonic method. Based on the obtained results, a statistical analysis and an estimation of parameters fc and ε0 were carried out, and an analytical model was proposed to describe σ-ε relationship for HSC reinforced with short straight fibers under compressive loading. The results obtained for compressed fiber-reinforced concrete were compared with data available in literature.

Thermal shakedown in granular materials with irregular particle shapes

Granular materials with irregular particle shapes undergo a myriad of temperature variations in natural and engineered systems. However, the impacts of cyclic temperature variations on the mechanics of granular materials remain poorly understood. Specifically, little is known about the response of granular materials to cyclic temperature variations as a function of the following central variables: particle shape, applied stress level, relative density, and temperature amplitude. This paper presents advanced laboratory experiments to explore the impacts of cyclic temperature variations on the mechanics of granular materials, with a focus on sands. The results show that cyclic temperature variations applied to sands induce thermal shakedown: the accumulation of irreversible bulk deformations due to microstructural rearrangements caused by thermal expansions and contractions of the constituting particles. The deformation of sands caused by thermal shakedown strongly depends on particle shape, stress level, relative density, and temperature amplitude. This deformation is limited for individual thermal cycles but accumulates and becomes significant for multiple thermal cycles, leading to substantial compaction in sands and other granular materials, which can affect various natural and engineered systems.

The value of marsh restoration for flood risk reduction in an urban estuary

The use of nature-based solutions (NBS) for coastal climate adaptation has broad and growing interest, but NBS are rarely assessed with the same rigor as traditional engineering solutions or with respect to future climate change scenarios. These gaps pose challenges for the use of NBS for climate adaptation. Here, we value the flood protection benefits of stakeholder-identified marsh restoration under current and future climate change within San Francisco Bay, a densely urbanized estuary, and specifically on the shores of San Mateo County, the county most vulnerable to future flooding in California. Marsh restoration provides a present value of $21 million which increases to over $100 million with 0.5 m of sea level rise (SLR), and to about $500 million with 1 m of SLR. There are hotspots within the county where marsh restoration delivers very high benefits for adaptation, which reach $9 million/hectare with likely future sea level and storm conditions. Today's investments in nature and community resilience can result in increasing payoffs as climate change progresses and risk increases.

GPS/BDS triple-frequency cycle slip detection and repair based on moving window global search method

Cycle slip detection and repair are crucial steps in achieving high accuracy in Global Navigation Satellite System (GNSS) data processing. The use of Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) triple frequency observations allows for more accurate detection and repair of cycle slips compared to single or dual frequency. This study presents a moving window global search method by selecting three sets of combined coefficients to construct geometry-free (GF) models to minimize the influence of the ionosphere, using a moving window to update the standard deviation of cycle slip estimation, applying the "3 σ " criterion to constrain the range, and utilizing a global search method to detect and repair triple-frequency cycle slips. Through five sets of 1 Hz GNSS data experiments, the results demonstrate the effectiveness of this method in determining the position and size of triple-frequency cycle slips while avoiding multi-value problems. The detection success rate for GPS ranges from 98.0 to 100.0%, while BDS ranges from 92.0 to 100.0%. On average, GPS achieves a detection rate of 99.2%, and BDS reaches 96.7%, which is 0.8% and 1.8% higher than the direct rounding method, respectively. Compared to existing methods, it is also effective for the vast majority of small cycle slips within 2 cycles.

Large-scale modular and uniformly thick origami-inspired adaptable and load-carrying structures

Existing Civil Engineering structures have limited capability to adapt their configurations for new functions, non-stationary environments, or future reuse. Although origami principles provide capabilities of dense packaging and reconfiguration, existing origami systems have not achieved deployable metre-scale structures that can support large loads. Here, we established modular and uniformly thick origami-inspired structures that can deploy into metre-scale structures, adapt into different shapes, and carry remarkably large loads. This work first derives general conditions for degree-N origami vertices to be flat foldable, developable, and uniformly thick, and uses these conditions to create the proposed origami-inspired structures. We then show that these origami-inspired structures can utilize high modularity for rapid repair and adaptability of shapes and functions; can harness multi-path folding motions to reconfigure between storage and structural states; and can exploit uniform thickness to carry large loads. We believe concepts of modular and uniformly thick origami-inspired structures will challenge traditional practice in Civil Engineering by enabling large-scale, adaptable, deployable, and load-carrying structures, and offer broader applications in aerospace systems, space habitats, robotics, and more.

Experimental research on mechanical performance of grouting plugging material with large amount of fly ash

In order to achieve the purpose of long-term stable mining of roadway, the strength and stability of rock mass are improved by means of grouting of fractured rock mass. In this paper, orthogonal test and numerical simulation methods were used to study the plugging performance of large amount of fly ash grouting slurry. The fluidity, water separation rate, compressive strength, setting time, stone rate and viscosity of the slurry were analyzed, and the optimal slurry ratio scheme was obtained. Under the optimal ratio scheme, the slurry transport process of the fractured rock mass was simulated, and the dynamic evolution law of the permeability of the slurry in the fractured rock mass was obtained. The study shows that the proportions of fly ash, ordinary Portland cement, loess, accelerant, expansion agent, bentonite water reducer and solidifying agent were 52.65%, 27.70%, 13.85%, 3%, 0.7%, 0.8%, 0.6% and 0.7% in the slurry ratio scheme, respectively. The slurry migration in the fractured rock mass experienced three stages, namely the filling and diffusion stage, the percolation and deposition stage and the sealing stage. The initial permeability was 971.9 mD and decreased to 45.79 mD after 1800 s, with a decrease of 95.3%. The slurry sealing performance was significantly improved, which has certain guiding significance for the application of underground grouting reinforcement engineering.

A Georeferenced Dataset for Mapping and Assessing Subgrade Defects in China's High-Speed Railways

China has the world's longest high-speed rail (HSR) network, marked by dense transportation and complex operations. However, frequent train use coupled with extreme weather conditions has led to rising subgrade issues. Existing railway defect records suffer from inconsistency, hindering direct applicability. Currently, there is a lack of a relevant dataset dedicated to HSR subgrade defects. To bridge this gap, we developed a comprehensive georeferenced dataset that encompasses defect records extracted from peer-reviewed literature published between 1999 and 2023 in China. Rigorous quality control procedures were implemented to eliminate duplicate data and ensure the accuracy of the dataset. The dataset consists of georeferenced records for eight different defects, spanning across 661 locations and categorized at various scales, ranging from provinces to townships. The most commonly reported defect types include subgrade settlement, frost damage, uplift deformation, and mud pumping. This dataset provides a comprehensive map of historical subgrade defects affecting high-speed railways in China. It could facilitate operational risk assessments and the prediction of subgrade performance.

Numerical analyses for three-dimensional face stability of circular tunnels in purely cohesive soils with linearly increasing strength

The tunnel face stability in purely cohesive soils with linearly increasing strength was investigated using three-dimensional finite element limit analysis (FELA). Both the collapse (active failure) and blow-out (passive failure) of the tunnel face were considered in the analysis. The rigorous upper bound (UB) and lower bound (LB) solutions of the load factor were calculated with a wide range of ground conditions to cover a broad scope of practical application. The results showed that the whole surface of the face is at failure in the collapse case; while in the blow-out case, there exists a gradual evolution process from partial failure to global failure within the tunnel face with increasing buried depth. Later, based on 960 finite element limit analysis results, a series of practical equations were proposed for tunnel face stability analysis in purely cohesive soils. These equations can be employed to quickly calculate the UB and LB solutions of the limit support pressure and the stability number of a tunnel face in both the collapse and blow-out cases. Finally, the calculation results from these equations were compared with those from previous studies in detail. The comparisons showed that the proposed equations make an improvement over existing methods and can be used as an efficient tool in practical engineering.

Low energy consumption layout of exhibition buildings in Yangtze River Delta region

The issue of high energy consumption in exhibition buildings has long been a focal point in the field of architectural design. However, current energy consumption assessments for exhibition buildings mainly focus on post-construction evaluations, lacking corresponding guidance during the initial design phase. To address this issue, this study selected 48 well-known exhibition buildings both domestically and internationally as research subjects. Utilizing scatter plot curve fitting, it was discovered that there exists a nonlinear quadratic relationship between the building area of the first floor and the courtyard area. Based on this relationship, four typical layouts were established to match the climatic characteristics of Hangzhou, a representative region in the Yangtze River Delta of China. Taking into account regional architectural features, the study specifically examined the impact of different orientations and window-to-wall ratios on energy consumption levels. The influence of these factors on energy consumption was analyzed using the DesignBuilder software. The results revealed that there exists an optimal window-to-wall ratio for exhibition buildings, with parallel, L-shaped enclosed south-facing courtyards, and U-shaped enclosed east-facing courtyards showing greater energy efficiency. This research provides guidance for designing exhibition buildings that are energy-efficient and foster a harmonious indoor-outdoor relationship.

The influence of anthropogenic regulation and evaporite dissolution on earthquake-triggered ground failure

Remote sensing observations of Searles Lake following the 2019 moment magnitude 7.1 Ridgecrest, California, earthquake reveal an area where surface ejecta is arranged in a repeating hexagonal pattern that is collocated with a solution-mining operation. By analyzing geologic and geotechnical data, here we show that the hexagonal surface ejecta is likely not a result of liquefaction. Instead, we propose dissolution cavity collapse (DCC) as an alternative driving mechanism. We support this theory with pre-event Interferometric Synthetic Aperture Radar data, which reveals differential subsidence patterns and the creation of subsurface void space. We also find that DCC is likely triggered at a lower shaking threshold than classical liquefaction. This and other unknown mechanisms can masquerade as liquefaction, introducing bias into liquefaction prediction models that rely on liquefaction inventories. This paper also highlights the opportunities and drawbacks of using remote sensing data to disentangle the complex factors that influence earthquake-triggered ground failure.

A comparative analysis of RCC and composite buildings using the new plastic deformation (PD) method

Low computational efficiency and non-linearity behaviour make the simulation of the overall building structure problematic to attain with a single dynamic or static method. Thus, this paper uses a plastic deformation (PD) method based on concrete plasticity theory (CPT) for comparative analysis of multi-storey reinforcement cement concrete (RCC) and composite buildings under common and rare earthquake loads. For this purpose, a 15-storey tall building was selected for analysis using ABAQUS software. At first, a possible building model was created and then plastic deformation analysis was performed using the new PD method under both common and rare earthquakes. After that, a nonlinear time history analysis was conducted, and the results of plastic strain distribution, lateral displacement, peak acceleration, storey stiffness, shear force, storey drift, normalised shear, and top deflection of the RCC and composite buildings were studied deeply. The fundamental time period of the RCC model was found to be 5.2 s while the fundamental time period of the composite model was 6 s. Under common and rare earthquake leads, the peak acceleration of the RCC building was 19% and 22% higher than composite buildings, respectively. Under common and rare seismic loads, the top deflections of the composite building were 33% and 36% higher than those of RCC buildings, respectively. In the case of the RCC building, it was found in this study that higher peak acceleration (PA) of the ground motion led to higher storey top displacement, storey drift, shear force and top deflection under both ground motions. Numerical results suggested that the use of composite structure is more durable than RCC structure. It was also concluded that the PD method could also be effectively used for the analysis of RCC and composite buildings under dynamic loads.

Small-scale regional engineering geological study of the Czech Republic evaluating the relationship between slope gradients and engineering geological zones

The aim of the small-scale regional engineering geological study of the Czech Republic was to evaluate the relationship between slope gradient and engineering geological zones. The research motivation was to determine the average slope gradient, 25%, 50% (median) and 75% quantiles related to the different engineering geological zones. This scientific information is critical from the perspectives of engineering geology, geotechnical engineering, and foundation engineering because an increasing slope gradient evokes the need to create a cut respectively foundation excavation or another excavation in the geological structure, which increases the probability of occurrence of the problem in terms of differential settlement and bearing capacity of the structures. The research was carried out in the territory of the Czech Republic in 8 Quaternary zones with soil foundation ground and 10 pre-Quaternary zones with rocks and semi-rocks and their eluvia. A significant difference in the statistical characteristics of slope gradients was found in the group of Quaternary engineering geological zones (evaluated group I) compared to the group of pre-Quaternary zones (evaluated group II). The value range of the average slope gradient was 1.65° (16.9%) to 5.89° (60.3%) for the Quaternary engineering geological zones (soil foundation ground), representing 43.4% difference. Whereas for the over-quaternary engineering geological zones (rocks, semi-rocks and their eluvia), the difference was much higher, 3.59° (36.8%) to 9.76° (100%-value determined as a referential because it was the maximum), which is also reflected in a more significant percentage difference of 63.2%.

Modeling protective action decision-making in earthquakes by using explainable machine learning and video data

Earthquakes pose substantial threats to communities worldwide. Understanding how people respond to the fast-changing environment during earthquakes is crucial for reducing risks and saving lives. This study aims to study people's protective action decision-making in earthquakes by leveraging explainable machine learning and video data. Specifically, this study first collected real-world CCTV footage and video postings from social media platforms, and then identified and annotated changes in the environment and people's behavioral responses during the M7.1 2018 Anchorage earthquake. By using the fully annotated video data, we applied XGBoost, a widely-used machine learning method, to model and forecast people's protective actions (e.g., drop and cover, hold on, and evacuate) during the earthquake. Then, explainable machine learning techniques were used to reveal the complex, nonlinear relationships between different factors and people's choices of protective actions. Modeling results confirm that social and environmental cues played critical roles in affecting the probability of different protective actions. Certain factors, such as the earthquake shaking intensity and number of people shown in the environment, displayed evident nonlinear relationships with the probability of choosing to evacuate. These findings can help emergency managers and policymakers design more effective protective action recommendations during earthquakes.

Comparative study on fatigue evaluation of suspenders by introducing actual vehicle trajectory data

Suspenders play a crucial role in transmitting loads from the bridge deck to the main cable in a suspension bridge. They are susceptible to fatigue due to repeated dynamic loads, particularly traffic loads. Traffic Load Models (TLMs), typically created using Monte-Carlo simulation and Weigh-In-Motion (WIM) data, are employed to evaluate this fatigue. However, these models often overlook practical vehicle trajectories and spatio-temporal distribution, which compromises the precision of fatigue assessments. In this study, we introduce a novel 2D Intelligent Driver Model (2D-IDM) that incorporates actual vehicle trajectories, with a particular focus on transverse vehicle movement. This enhancement aims to improve the fidelity of existing TLMs. To provide a clear, qualitative, and quantitative understanding of the effects of fatigue evaluation with or without actual trajectory characteristics, we have structured this paper as a comparative study. We compare our proposed model, denoted as TLM S-3, with two observation-based models (O-1 and O-2) and two simulation-based models (S-1 and S-2). We conducted an experimental case study on a long-span suspension bridge, where the actual traffic load trajectory was obtained using a WIM-Vision integrated system. To calculate fatigue damage considering both longitudinal and transverse directions, we established a multi-scale Finite Element Model (FEM) using solid element types to simulate the bridge girder. This model can generate the stress influence surface of the bridge and has been verified in both static and dynamic aspects. Our detailed comparative analysis demonstrates the consistency of the proposed 2D-IDM with the actual measured traffic load trajectories. This indicates that our approach can enhance the fidelity and precision of fatigue evaluations for bridge suspenders.

Adoption of hooped-battens in cold-formed steel built-up columns for superior axial performance

Previous research on cold-formed steel (CFS) battened columns has identified the critical factors influencing their performance and accordingly, their limiting values for improved performance have been recommended. However, these studies involved connecting battens to the chords (channels) via their flanges, leaving the slenderest component disconnected from the web. This study introduces a novel hooped-batten (tubular-element) that links both webs and flanges of the chords together, thereby improving the structural integrity of the built-up system and curtailing the half-wave buckling length in the webs. As a result, axial strength and stability in these built-up columns may improve adequately. Firstly, a numerical model of a conventional CFS battened column was developed in ABAQUS and verified against test results on the same reported in literature. Afterward, the validated model was used to simulate the behaviour of CFS built-up columns with hooped-battens. Two key parameters i.e., unbraced chord slenderness and overall column slenderness were varied to explore their influence on the axial behaviour of built-up columns in terms of peak strengths, failure modes and load-displacement characteristics. The performance of the hooped-battened columns was compared with the identical conventional battened columns, which reflects that the former exhibits superior strength and stability characteristics over the latter.

Territorial exceedance of probabilistic seismic hazard from ShakeMap data

Current seismic structural design makes use of a ground motion intensity that has a certain probability of being exceeded at a site of interest in a time interval or, equivalently, exceedance return period. The design intensities with the same return period are often collected in the form of maps deriving from probabilistic seismic hazard analysis (PSHA) for each of the sites of interest. Probability theory underlying PSHA dictates that, in any time interval, design intensities are expected to be exceeded in a fraction of sites that depends on the return period the map refers to. In the case of Italy, three different nationwide PSHA studies can be currently considered of relevance. In the study, the estimated areal fraction of the Italian territory exposed to exceedance of the design intensity from 2008 to 2019 was quantified for the three hazard models, based on ShakeMap data for instrumental earthquakes. In addition, the same fraction was calculated considering a sparse catalog of inferred ShakeMap for historical earthquakes that occurred over almost 1000 years. It was found that, despite the apparent differences in the hazard models, the estimated fraction of territory exposed to exceedance is comparable for all the considered hazard maps.

Experimental study of energy‑absorbing and support characteristics of glass microsphere-filled steel tube columns under uniaxial compression

An innovative energy-absorbing and bearing structure was proposed, which incorporated the coupling of glass microspheres with a metal tube. Glass microsphere-filled steel tube (GMFST) column, consisting of external steel tube and inner glass microspheres, was expected to give full play to the energy-absorbing and load-bearing capacities of the particle while restricting particle flow from collapsing, thereby enhancing the overall structural strength. Four groups of steel tubes and the GMFST specimens were designed and subjected to axial compression tests at four different loading rates to investigate the performance of the structure. These tests aimed to analyze the deformation mode, mechanical response, and energy absorption capacity of the GMFST columns under quasi-static to low-speed compression conditions. The results indicated that the deformation process and failure mode of GMFST columns were similar to those of hollow steel tubes, albeit with a different post-buckling mode. Filling the steel tubes with glass microspheres reduced the load fluctuation range, moderated load-displacement curves, and exhibited a strain rate strengthening effect. The GMFST columns demonstrated superior energy absorption capacity, with significant increases in crush force efficiency, the averaged crush force, and the total absorbed energy, particularly in terms of subsequent support capacity. The load-increasing reinforcement properties enabled GMFST columns to overcome the limitations associated with the unstable post-buckling path of energy‑absorbing damping structure, exhibiting outstanding load-bearing performance and stability in the later stages. The results provided valuable guidelines for designing and engineering high-performance GMFST columns, serving as a new type of energy-absorbing and supporting structure.

The development of priority decision model for old urban community renovation in China

Old urban community renovation is an important task of urban renewal in China. In order to ensure the quality and efficiency of renovation work, the government requires a method to assess the priority for old urban community renovation. This paper proposes an evaluation model from a more comprehensive perspective. It establishes the evaluation index system with CIPP model. The method of Cloud-VIKOR is selected to construct the evaluation model. Finally, selects nine case communities to verify the evaluation model. The results show that the evaluation index system covers the whole process of renovation project and the evaluation indexes proposed in the existing research. The priority decision result of 9 case communities is basically consistent with the actual renovation sequence and does not change greatly due to the fluctuation of decision-making mechanism coefficient or the evaluation index weight. This evaluation model can help decision-makers diagnose and optimize the renovation project.

Traffic light optimization with low penetration rate vehicle trajectory data

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world.

Economic and environmental benefits of automated electric vehicle ride-hailing services in New York City

A precise, scalable, and computationally efficient mathematical framework is proposed for region-wide autonomous electric vehicle (AEV) fleet management, sizing and infrastructure planning for urban ride-hailing services. A comprehensive techno-economic analysis in New York City is conducted not only to calculate the societal costs but also to quantify the environmental and health benefits resulting from reduced emissions. The results reveal that strategic fleet management can reduce fleet size and unnecessary cruising mileage by up to 40% and 70%, respectively. This alleviates traffic congestion, saves travel time, and further reduces fleet sizes. Besides, neither large-battery-size AEVs nor high-power charging infrastructure is necessary to achieve efficient service. This effectively alleviates financial and operational burdens on fleet operators and power systems. Moreover, the reduced travel time and emissions resulting from efficient fleet autonomy create an economic value that exceeds the total capital investment and operational costs of fleet services.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

Optimising peak energy reduction in networks of buildings

Buildings are amongst the world's largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management-analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.

Integrative analysis of epilepsy-associated genes reveals expression-phenotype correlations

Epilepsy is a highly prevalent neurological disorder characterized by recurrent seizures. Patients exhibit broad genetic, molecular, and clinical diversity involving mild to severe comorbidities. The factors that contribute to this phenotypic diversity remain unclear. Here we used publicly available datasets to systematically interrogate the expression pattern of 230 epilepsy-associated genes across human tissues, developmental stages, and central nervous system (CNS) cellular subtypes. We grouped genes based on their curated phenotypes into 3 broad classes: core epilepsy genes (CEG), where seizures are the dominant phenotype, developmental and epileptic encephalopathy genes (DEEG) that are associated with developmental and epileptic encephalopathy, and seizure-related genes (SRG), which are characterized by the presence of seizures and gross brain malformations. We find that compared to the other two groups of genes, DEEGs are highly expressed within the adult CNS, exhibit the highest and most dynamic expression in various brain regions across development, and are significantly enriched in GABAergic neurons. Our analysis provides an overview of the expression pattern of epilepsy-associated genes with spatiotemporal resolution and establishes a broad expression-phenotype correlation in epilepsy.

THM model of rock tunnels in cold regions and numerical simulation

The freezing damage of rock tunnels in cold region involves ice-water phase change and complicated interaction of Thermo-Hydro-Mechanical (THM) field. Taking the fractured rock mass of cold region tunnels as research subject, the THM coupling model of cold region tunnels was established, which is based on the seepage mechanics, heat transfer theory, damage mechanics and equivalent continuum theory. This model could reflect the anisotropic properties of deformation, water migration and heat transfer caused by the initial fracture of rock mass. The construction and operation processes of a rock tunnel in cold region were simulated, and results were compared with the measured value and predecessor's achievements. It shows that proposed model could reflect the anisotropic property of surrounding rock and the simulated deformation and stress are not symmetrical. Compared with the literature, the calculated results in this paper are closer to the measured values. The insulating layer has a significant effect on the stress of the supporting structures. The maximum tension stress of the lining is 4.5 times as that without insulating layer, and the lining will be destroyed for the overlarge tension stress.

Migration mechanism of grouting slurry and permeability reduction in mining fractured rock mass

In order to solve the water and gas discharge hazard caused by gob water and harmful gases (such as CO), the method of grouting overburden fractures is adopted to achieve the purpose of safe and efficient mining production in coal mines. This paper carries out the experimental research on the permeability reduction effect of grouting in fractured rock mass, expounds the relationship between gas flow rate and pressure gradient, seepage pressure and permeability, confining pressure and permeability, and analyzes the permeability change law of fractured rock mass before and after grouting. Besides, the grouting migration and permeability reduction model of fractured fine-grained sandstone is constructed by combining grouting test and numerical simulation, which reveals the dynamic evolution law of rock mass permeability in the grouting process. The results show that the permeability of the grouting rock sample decreases from 700-13,000 to 15-300 mD than that of the ungrouting rock sample, and the decrease is more than 95%, which indicates that the sealing performance of grouting slurry is better. Besides, numerical simulations show that the initial permeability of rock samples is 971.9 mD, and the permeability decreases to 45.79 mD after 1800s, and the permeability decreases to 95.3%, which is basically consistent with the experimental results after grouting. The greater the grouting pressure is, the better the grouting effect is. With the increase of the grouting pressure, the increase of the grouting effect is no longer obvious.

Tunnel construction in shallow soft rock using the pipe shed support

In order to clarify the impact mechanism of shallow buried soft rock tunnel excavation on the upper existing highway, as well as the mechanism of pipe shed reinforcement, a combination of theoretical analysis and on-site monitoring was used to conduct in-depth research on the Diantou Tunnel Crossing the existing highway project of Dayong Expressway. The impact of shallow buried soft rock tunnel crossing construction on the existing highway and safety control issues were studied, a new deformation control index, namely deformation difference rate, was introduced. The results show that the main lateral area of influence of rock deformation is within a distance of one diameter, and the overall area of influence is within a distance of twice the diameter. From the perspective of axial tunnel deformation, the deformation of surrounding rock tends to be stable when the excavation face passes through the monitoring section about twice the tunnel diameter. Effectively controlling rock deformation depends on the total amount of settlement deformation, the change rate of deformation, and the difference rate of deformation. For V-class shallow buried tunnel surrounding rock, in order to prevent cracks on the surface, the cumulative deformation of the surrounding rock needs to be less than 50 mm, the change rate needs to be less than 3 mm/day, and the difference rate needs to be less than 5 mm/m. Finally, the proposed control standard for surface subsidence is given for similar engineering reference.

Mapping urban mobility using vehicle telematics to understand driving behaviour

Telematics data, primarily collected from on-board vehicle devices (OBDs), has been utilised in this study to generate a thorough understanding of driving behaviour. The urban case study area is the large metropolitan region of the West Midlands, UK, but the approach is generalizable and translatable to other global urban regions. The new approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM) is used to convert telematics data into driving metrics, including the relative time the vehicle fleet spends idling, cruising, accelerating, and decelerating. The telematics data is also used to parameterize driving volatility and aggressiveness, which are key factors within road safety, which is a global issue. Two approaches to defining aggressive driving are applied and assessed, they are vehicle jerk (the second derivative of vehicle speed), and the profile of speed versus acceleration/deceleration. The telematics-based approach has a very high spatial resolution (15-150 m) and temporal resolution (2 h), which can be used to develop more accurate driving cycles. The approach allows for the determination of road segments with the highest potential for aggressive driving and highlights where additional safety measures could beneficially be adopted. Results highlight the strong correlation between vehicle road occupancy and aggressive driving.

Using symbolic machine learning to assess and model substance transport and decay in water distribution networks

Drinking water infrastructures are systems of pipes which are generally networked. They play a crucial role in transporting and delivering clean water to people. The water quality analysis refers to the evaluation of the advective diffusion of any substance in drinking water infrastructures from source nodes. Such substances could be a contamination for the system or planned for the disinfection, e.g., chlorine. The water quality analysis is performed by integrating the differential equation in the pipes network domain using the kinetics of the substance decay and the Lagrangian scheme. The kinetics can be formulated using a specific reaction order depending on the substance characteristics. The basis for the integration is the pipes velocity field calculated by means of hydraulic analysis. The aim of the present work is to discover the intrinsic mechanism of the substance transport in drinking water infrastructures, i.e., their pipes network domain, using the symbolic machine learning, named Evolutionary Polynomial Regression, which provides "synthetic" models (symbolic formulas) from data. We demonstrated, using one real network and two test networks, that the concentration at each node of the network can be predicted using the travel time along the shortest path(s) between the source and each node. Additionally, the formula models provided by symbolic machine learning allowed discovering that a unique formula based on kinetic reaction model structure allows predicting the residual substance concentration at each node, given the source node concentration, surrogating with a good accuracy the integration of the differential equations.

Impact of material supply chain on the productivity optimization for the construction of roads projects

Materials require special consideration when developing a project plan because they make up such a sizable chunk of the overall budget. Materials supply and delivery are crucial especially in road construction projects as they are required for the daily construction process. Lack of materials is a major source of jobsite productivity loss. This is due to the lack of structured communication and clearly defined tasks in the current materials management methods. The divergence between design and construction, the failure to coordinate and integrate multiple functional specializations, and poor communication lead to excessive fragmentation. All of these contribute to performance issues like late material ordering and delivery, low productivity, and budget overruns. This research develops a material supply chain (MSC) framework for best practices in road construction projects at all phases. This ensures that contractors receive the supplies they need at the optimum time, with the required quantities, and at the lowest possible cost. Contractors can enhance output, save money, and stay competitive. A questionnaire was designed to investigate current practices in MSC, identify the most common obstacles that faced contractors throughout the project phases, and identify the most important contributors to the integration of supply chain in construction. The developed framework was then evaluated by road construction experts; 90% stated that the proposed framework promotes project participants to share information and data. 80% assured that the framework promotes completing the project with desired quality and encourages problem solving before it even occurs.

Application of a novel metaheuristic algorithm inspired by stadium spectators in global optimization problems

This paper presents a novel metaheuristic algorithm inspired by the actions of stadium spectators affecting behavior of players during a match which will be called stadium spectators optimizer (SSO) algorithm. The mathematical model of the SSO algorithm is presented and the performance and efficiency of the presented method is tested on some of the well-known mathematical test functions and also CEC-BC-2017 functions. The SSO algorithm is a parameter-free optimization method since it doesn't require any additional parameter setup at any point throughout the optimization process. It seems urgently necessary to design a novel metaheuristic algorithm that is parameter-free and capable of solving any optimization problem without taking into account extra parameters, as the majority of metaheuristic algorithms rely on the configuration of extra parameters to solve different problems efficiently. A positive point for the SSO algorithm can be seen in the results of the suggested technique, which indicate a partial improvement in performance. The results are compared with those of golf optimization algorithm (GOA), Tiki taka optimization algorithm (TTA), Harris Hawks optimization algorithm (HHO), the arithmetic optimization algorithm (AOA), CMA-ES and EBOwithCMAR algorithms. The statistical tests are carried out for the obtained results and the tests reveal the capability of the presented method in solving different optimization problems with different dimensions. SSO algorithm performs comparably and robustly with the state-of-the-art optimization techniques in 14 of the mathematical test functions. For CEC-BC-2017 functions with ten dimensions, EBOwithCMAR performs better than the proposed method. However, for most functions of CEC-BC-2017 with ten dimensions, the SSO algorithm ranks second after EBOwithCMAR, which is an advantage of the SSO since the proposed method performs better than the well-known CMA-ES optimization algorithm. The overall performance of the SSO algorithm in CEC-BC-2017 functions with 10 dimensions was acceptable, in dimension of 30, 50 and 100, the performance of the proposed method in some functions decreased.

Stability analysis of loess fill slope supported by frame prestressed anchors considering tensile strength cut-off

In the stability analysis of loess fill slope, the fissure nature of loess is often ignored, which makes the stability calculation of fill slope too conservative. Based on the upper limit theory of plastic limit analysis, the stability analysis model of loess-filled fissured slope supported by frame prestressed anchors was established. Considering the tensile strength cut-off yield property of soil, the stability coefficient of slope was calculated, and the influence of different factors on slope stability was analyzed. The results show that ignoring the fissures in loess will overestimate the stability of the fill slope, and the support structure can significantly improve the stability of the loess-filled fissure slope. The research results of this paper can further enrich the stability analysis theory of loess-filled fissured slope supported by frame prestressed anchors, which is of great significance to guide engineering practice.

Identifying and ranking of CMIP6-global climate models for projected changes in temperature over Indian subcontinent

Selecting the best region-specific climate models is a precursor information for quantifying the climate change impact studies on hydraulic/hydrological projects and extreme heat events. A crucial step in lowering GCMs simulation-related uncertainty is identifying skilled GCMs based on their ranking. This research performed a critical assessment of 30 general circulation models (GCMs) from CMIP6 (IPCC's sixth assessment report) for maximum and minimum temperature over Indian subcontinent. The daily temperature data from 1965 to 2014 were considered to quantify maximum and minimum temperatures using a gridded spatial resolution of 1°. The Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), Perkins skill score (PSS), normalized root mean square error (NRMSE), and absolute normalized mean bias error (ANMBE) were employed as performance indicators for two different scenarios, S1 and S2. The entropy approach was used to allocate weights to each performance indicator for relative ranking. Individual ranking at each grid was achieved using a multicriteria decision-making technique, VIKOR. The combined ranking was accomplished by integrating group decision-making, average ranking perspective, and cumulative percentage coverage of India. The outcome reveals that for S1 and S2, NRMSE and NSE are the most significant indicators, respectively whereas CC is the least significant indicator in both cases. This study identifies ensemble of KIOST-ESM, MRI-ESM2-0, MIROC6, NESM3, and CanESM5 for maximum temperature and E3SM-1-0, NESM3, CanESM5, GFDL-CM4, INM-CM5-0, and CMCC-ESM2 for minimum temperature.

A method for rapid demolition of a crossline arch bridge: a case study

To reduce the adverse impact of demolition of the symmetrical rigid frame arch bridge overcrossing the highway with over-saturated traffic flow, a rapid demolition method based on the Self-Propelled Modular Transporter (SPMT) technique was developed in this study. The calculation formulae for reaction forces of the supporting brackets, as well as driving force and stability of SPMTs, were derived by analyzing the stability, synchronization, and influencing parameters of the cut bridge body-transport system. In addition, a monitoring system during the whole process was developed to ensure the demolition safety. An application of demolishing a crossline symmetrical rigid frame arch bridge in China within 5 h has been presented. The results showed that the proposed method can be successfully applied in real projects, leading to significant reduction in traffic impact, energy consumption, and environmental pollution.

Evaluating the pedestrian level of service for varying trip purposes using machine learning algorithms

The pedestrians' feeling of comfort while walking on footpaths varies according to the time of day, environment, and the purpose of the trip. The quality of service offered by pedestrian facilities such as walkways, intersections, and public places is evaluated by the Pedestrian level of service (PLOS) and has been measured from time to time, to upgrade and maintain the sustainable travel choice of people. This paper aims to focus on the level of service based on three main trip purposes such as work, education, and recreation, while considering various path characteristics and pedestrian flow characteristics that affect the pedestrian's feeling of comfort on the walkways. The data has been collected using pedestrian questionnaire surveys and pedestrian sensors in the Melbourne central business district and the significant factors that influence the PLOS for each trip purpose will be chosen using the Mutual Information gain, which is found to be different for each trip purpose. The major influencing factors that affect the PLOS will be used to develop machine learning models for three trip purposes separately using Random Forest and Light-GBM algorithm in Python. The accuracy of prediction using the light GBM model is 0.74 for education, 0.80 for recreation, and 0.70 for work trip purposes. It is found using SHAP which stands for Shapely Additive explanations that the factors such as interpersonal distance, distance from vehicles, construction sites, vehicle volume, traffic noise, and footpath surface are the most influencing variables that affect the PLOS based on three different trip purposes.

The shape function method of nonlinear thermal stress of granite fracture tips in a high-temperature environment

Exposed rock masses in tunnel portals are susceptible to thermal deterioration in southern China, where temperatures are relatively high. The thermal stress field of rock masses is affected by fracture shape and distribution as fractures near the surface are channels for solar radiation energy to be converted into rock thermal energy. In this study, a function expression is developed for triangular heat sources of fractured rock masses in a tunnel portal in a high-temperature environment. By the function expression, the temperature field and thermal stress field are calculated, and the influence of fracture shape parameters and multi-fracture interaction is analyzed. The results are as follows: (1) the temperature field and thermal stress field of exposed rocks are redistributed by fractures. The internal temperature of the fractured rocks is higher than that of non-fractured rocks, and thermal stress near the fracture tip increases. (2) For triangular fractures of the same length, thermal stress increases as the apex angle increases. (3) When the spacing between parallel fractures or coplanar fractures is close, the superposition effect of thermal stress becomes significant. (4) In a high-temperature environment, temperature field and thermal stress field of a fractured rock are both nonlinear as temperature and thermal stress around fractures increase significantly. The results provide effective reference for stability evaluation of fractured rock masses in tunnel portals and offer theoretical foundation for thermal diseases analysis and protection measures of tunnel engineering in high-temperature environments of southern China.

Accurate estimation of concrete consumption in tunnel lining using terrestrial laser scanning

Accurate estimation of concrete (including shotcrete) consumption plays a crucial role in tunnel construction. A novel method has been introduced to accurately estimate concrete consumption with terrestrial laser scanning (TLS). The estimation needs to capture TLS data of tunnel surfaces at different stages of construction. Unrolling point clouds, a novel two-stage algorithm consisting of noise removal and hole filling has been used to generate resampled points. Furthermore, resampled points from two scans (before and after lining construction) ultimately generate an innovative computation model composed of multiple hexahedral elements, which is used for calculating volumes. The proposed technique was applied to the Tiantaishan highway tunnel and Da Fang Shan high-speed railway tunnel. The calculation relative error of the rebound rate is 0.19%, and the average relative error in predicting the demand for secondary lining concrete is 0.15%. Compared with 3D Delaunay with curve fitting, the proposed technique offers a more straightforward operation and higher accuracy. Considering factors such as tunnel geometry, support design, and concrete properties, a computational model will provide valuable insights into optimizing resource allocation and reducing material waste during construction.

A collapse risk assessment method for subway foundation pit based on cloud model and improved Dempster-Shafer evidence theory

Collapse is a major engineering hazard in open-cut foundation pit construction, and risk assessment is crucial for considerably reducing engineering hazards. This study aims to address the ambiguity problem of qualitative index quantification and the failure of high-conflict evidence fusion in risk assessment. Thus, a fast-converging and high-reliability multi-source data fusion method based on the cloud model (CM) and improved Dempster-Shafer evidence theory is proposed. The method can achieve an accurate assessment of subway pit collapse risks. First, the CM is introduced to quantify the qualitative metrics. Then, a new correction parameter is defined for improving the conflicts among evidence bodies based on conflict degree, discrepancy degree and uncertainty, while a fine-tuning term is added to reduce the subjective effect of global focal element assignment. Finally, the risk assessment result is obtained according to the maximum affiliation principle. The method is successfully applied to Luochongwei Station, where the difference between the maximum value and the second largest value of the basic probability assignment is 0.624, and the global uncertainty degree is 0.087. Both values satisfy the decision evaluation condition; however, values of other methods only satisfy one or neither condition. In addition, the proposed method requires only four cycles to reach the steady state by fusing data of the same index, which has faster convergence compared with that of other methods. The proposed method has good universality and effectiveness in subway pit collapse risk assessment.

Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil

The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.

Ventilation control of road tunnels towards disturbance suppression

In recent years, research on ventilating tunnels has become increasingly important. However, the impact of external disturbances on ventilating systems has been largely ignored. To address this issue of frequent airflow fluctuations caused by external perturbations, which cannot be fully compensated using conventional control methods, this study proposes a perturbation-compensated ventilation control approach. A disturbance compensator is developed by incorporating the tunnel's airflow velocity and the number of jet fan start-stop events as input parameters. By compensating for external disturbances, the disturbance to the system is reduced. The Simulink model of the tunnel controller was used for simulation experiments. The compensator demonstrated good tracking results in comparison experiments with different disturbances. The ventilation approach based on disturbance compensator is capable of regulating the fluctuation of CO concentration within a justifiable range compared to using PID control and ADRC. This not only improves the stability of the entire control system but also significantly prolongs the service life of the jet fan by reducing the frequency of start-stop cycles.

Potholes and traffic signs detection by classifier with vision transformers

Detecting potholes and traffic signs is crucial for driver assistance systems and autonomous vehicles, emphasizing real-time and accurate recognition. In India, approximately 2500 fatalities occur annually due to accidents linked to hidden potholes and overlooked traffic signs. Existing methods often overlook water-filled and illuminated potholes, as well as those shaded by trees. Additionally, they neglect the perspective and illuminated (nighttime) traffic signs. To address these challenges, this study introduces a novel approach employing a cascade classifier along with a vision transformer. A cascade classifier identifies patterns associated with these elements, and Vision Transformers conducts detailed analysis and classification. The proposed approach undergoes training and evaluation on ICTS, GTSRDB, KAGGLE, and CCSAD datasets. Model performance is assessed using precision, recall, and mean Average Precision (mAP) metrics. Compared to state-of-the-art techniques like YOLOv3, YOLOv4, Faster RCNN, and SSD, the method achieves impressive recognition with a mAP of 97.14% for traffic sign detection and 98.27% for pothole detection.