Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson's Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values.

Carbonated ground granulated blast furnace slag stabilising brown kaolin

Proposals have been made by several researchers to conduct the sequestration of carbon dioxide (CO₂) through calcium and magnesium-rich materials. From these materials, ground granulated blast furnace slag (GGBS) containing 5% magnesium and 45% calcium is seen to be a good candidate and is available to sequester CO₂. This study intends to ascertain the ability to absorb CO₂, sequester it, and increase treated kaolin strength with different content of GGBS under various carbonation periods with varying CO₂ pressure. The impacts of carbonated GGBS on the mechanical attributes of soil were examined by conducting the unconfined compressive strength (UCS) test, and microstructure analysis was conducted to identify the changes in the structure and Crestline phase. Stationary carbonation in a triaxial test with pure CO₂ was conducted to accelerate the carbonation process. The outcome indicates that the strength rises as the carbonation period rises. Likewise, UCS rises as the CO₂ pressure rises from 100 to 200 kPa. It could be concluded that augmentation of the strength is because of carbonated calcium and magnesium products which stuff the soil voids. Changes occur on the microstructure level due to carbonation as well.

Carbon emissions from the peat fire problem-a review

Peat fires in tropical peatland release a substantial amount of carbon into the environment and cause significant harm to peatlands and the ecology, resulting in climate change, biodiversity loss, and the alteration of the ecosystem. It is essential to understand peat fires and to develop more effective methods for controlling them. To estimate carbon emissions and monitor fires, the depth of burning can measure the overall burnt down the volume, which is proportional to the carbon emissions that are emitted to the environment. The first step is to understand the technique of measuring the depth of the burn. However, there is a lack of integrated information regarding the burning depth for peat fires. This review paper discusses the techniques used to measure the burning depth, with particular attention given to quantifying carbon emissions. The article also provides information on the types of methods used to determine the burning depths. This research contributes to the field of peat fire by providing a readily available reference for practitioners and researchers on the current state of knowledge on peat fire monitoring systems.

Ground improvement and its role in carbon dioxide reduction: a review

Environmental global issues affecting global warming, such as carbon dioxide (CO2), have attracted the attention of researchers around the world. This paper reviews and discusses the ground improvement and its contribution to reducing CO2 in the atmosphere. The approach is divided into three parts: the Streamlined Energy and Emissions Assessment Model (SEEAM), the replacement of soil stabilisation materials that lead to the emission of a large amount of CO2 with alternatives and mineral carbonation. A brief discussion about the first two is reviewed in this paper and a detailed discussion about mineral carbonation and its role in enhancing soil strength while absorbing a large amount of CO2. It is emphasised that natural mineral carbonation requires a very long time for a material to reach its full capacity to form CO2; as a result, different acceleration processes can be done from increasing pressure, temperature, the concentration of CO2 and the addition of various additives. In conclusion, it was found that magnesium is more attractive than calcium, and calcium is complicated in terms of strength behaviour. Magnesium has a larger capacity for CO2 sequestration and it has a greater potential to increase soil strength than calcium.

A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern.

The contribution of particle swarm optimization to three-dimensional slope stability analysis

Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes.