A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities

Potential features of building information modeling (BIM) for application of project management knowledge areas in the construction industry

The construction industry (CI) plays a vital role in infrastructure development and improves the socio-economic status with employment opportunities and contribution to gross domestic progress (GDP) of countries. However, its productivity has diminished in recent years due to increasing complexities in construction projects (CPs) and lack of adoption of novel technologies such as Building Information Modeling (BIM). Also, there is a significant need of polishing the capabilities of construction practitioners to meet the project requirements in agreement with project management knowledge areas (PMKAs). This study, therefore, focused on identification and evaluation of factors necessary for measurement of extent of application of PMKAs. Subsequently, noteworthy features of BIM helpful for enhancing the capabilities of project managers (PMs) in application of PMKAs were identified from literature. A total of thirty-three factors for measurement of extent of application of PMKAs and sixty-six features of BIM helpful in enhancing the capabilities of PMs in application of PMKAs were found. The detailed study and analysis of these ninety-nine factors with the help of previous studies suggested that extent of application of PMKAs is measured with three sub-tasks i.e., plan, manage/develop, and monitor/control. In addition, by virtue of remarkable features and services of BIM, it helps in enhancing the capabilities of PMs in applying PMKAs: project integration, scope, cost, time, quality, resource, communications, procurement, risk, safety, and stakeholder management.

Investigating smart city adoption from the citizen's insights: empirical evidence from the Jordan context

This study aims to investigate the factors that perceive citizens' intention to adopt smart city technologies in the Arab world. A self-administered questionnaire that included 312 end users as citizens in Amman, Jordan's capital city, was used in this study. This study uses advanced statistical techniques to test an expanded technology acceptance model (TAM) that incorporates the determinants of perceived usefulness, perceived ease of use, security and privacy, ICT infrastructure and inadequate Internet connectivity, social influence, and demographic profiles. Based on the results, perceived ease of use and ICT infrastructure and Internet connectivity showed positive association with the intention of citizens to adopt smart city services in Jordan. By recognizing the factors that predict citizens' adoption of smart city services, this study presents some theoretical implications and practical consequences related to smart city service adoption.

Combining the YOLOv4 Deep Learning Model with UAV Imagery Processing Technology in the Extraction and Quantization of Cracks in Bridges

Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or over water, and is not easily accessible to a bridge inspector. Furthermore, poor lighting under bridges and a complex visual background can hinder inspectors in their identification and measurement of cracks. In this study, cracks on bridge surfaces were photographed using a UAV-mounted camera. A YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection. To perform the quantitative crack test, the images with identified cracks were first converted to grayscale images and then to binary images the using local thresholding method. Next, the two edge detection methods, Canny and morphological edge detectors were applied to the binary images to extract the edges of the cracks and obtain two types of crack edge images. Then, two scale methods, the planar marker method, and the total station measurement method, were used to calculate the actual size of the crack edge image. The results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm. The proposed approach can thus enable bridge inspections and obtain objective and quantitative data.

Blockchain feasibility assessment: A quantitative approach

This article outlines the research and development of a blockchain assessment framework which enables the assessment of the technical suitability, high-level design, adoption approach, economic feasibility, and business value potential of a blockchain solution with a particular organization for a specific process. The framework is a comprehensive, high-level, and generic assessment approach that enables better decision-making regarding blockchain exploration. Blockchain is a novel technology with the potential to disrupt several industries through its possession of many desirable functional characteristics, including, but not limited to, immutability, transparency, decentralization, and secure. Cryptocurrencies and these desirable characteristics have created hype around blockchain, consequently leading to blockchain projects with minimal understanding of what the technology is capable of and beneficial for, resulting in excessively high failure rates. Attempts have been made by researchers to reduce these high failure rates by creating a better understanding of blockchain, as well as creating assessment approaches. However, these approaches tend to apply to specific narrow use cases, or the approach is not comprehensive and only considers one aspect of blockchain assessment. This emphasizes the need for a comprehensive and generic blockchain assessment approach to aid with better decision-making regarding blockchain within organizations. This article aims at addressing this need by creating a blockchain assessment framework to aids with deciding whether it is worthwhile investing more time, effort, and money into blockchain exploration. The context of the study is set in the introduction, this is then followed by a brief explanation of the blockchain technology. Thereafter, the blockchain assessment framework is presented, followed by a brief explanation of the demonstration and validation of the framework using a case study and expert analysis. The framework is most valuable during the initial stages of blockchain exploration and creates momentum for further blockchain exploration in an organization. The study concludes with the limitations and future research recommendations.

Key Adoption Factors for Collaborative Technologies and Barriers to Information Management in Construction Supply Chains: A System Dynamics Approach

Construction processes are complex and dynamic. Like its other components, the construction supply chain (CSC) involves multiple stakeholders requiring varying levels of information sharing. In addition, the intensity and diversity of information in CSCs require dexterous management. Studies reveal that information complexity can be reduced using collaborative technologies (CTs). However, the barriers to information management (IM) hinder the CTs’ adoption process and cause complexity in CSCs. This research identifies barriers to IM and factors affecting the adoption of CTs in developing countries. In order to understand and address complexity, the system dynamics (SD) approach is adopted in this study. The aim is to investigate if SD can reduce information complexity using CTs. Causal loop diagrams (CLDs) were developed to understand the relationship between the IM barriers and CT adoption factors. The SD model, when simulated, highlighted three main components, i.e., complexity, top management support, and trust and cooperation, among others, as factors affecting the adoption of CTs. Addressing these factors will reduce information complexity and result in better IM in construction projects.

Blockchain-Based Land Registration System: A Conceptual Framework

Land registration authorities are frequently held accountable for the alleged mismanagement and manipulation of land records in various countries. Pakistan's property records are especially vulnerable to falsification and corruption because of the country's poverty. Different parties therefore claim varying degrees of authority over a specific piece of land. Given the fact that this data has been consolidated, it has become significantly more vulnerable to security threats. The goal of decentralized system research has been to increase the reliability of these systems. In order to fix the flaws of centralized systems, blockchain-based decentralized systems are currently in development. By using significant land record registration models as the basis for this research, we hope to create a proof-of-concept system or framework for future use. Pakistan's land registration agency will benefit from our proposed conceptual framework. For the Pakistani government to implement a decentralized land record registry system, we propose a conceptual framework that outlines the essential components.

Big Data in Construction: Current Applications and Future Opportunities

Big data have become an integral part of various research fields due to the rapid advancements in the digital technologies available for dealing with data. The construction industry is no exception and has seen a spike in the data being generated due to the introduction of various digital disruptive technologies. However, despite the availability of data and the introduction of such technologies, the construction industry is lagging in harnessing big data. This paper critically explores literature published since 2010 to identify the data trends and how the construction industry can benefit from big data. The presence of tools such as computer-aided drawing (CAD) and building information modelling (BIM) provide a great opportunity for researchers in the construction industry to further improve how infrastructure can be developed, monitored, or improved in the future. The gaps in the existing research data have been explored and a detailed analysis was carried out to identify the different ways in which big data analysis and storage work in relevance to the construction industry. Big data engineering (BDE) and statistics are among the most crucial steps for integrating big data technology in construction. The results of this study suggest that while the existing research studies have set the stage for improving big data research, the integration of the associated digital technologies into the construction industry is not very clear. Among the future opportunities, big data research into construction safety, site management, heritage conservation, and project waste minimization and quality improvements are key areas.

Automatic Target Detection from Satellite Imagery Using Machine Learning

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages

Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions.

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.

BIMp-Chart—A Global Decision Support System for Measuring BIM Implementation Level in Construction Organizations

Building Information Modeling (BIM) is recognized as one of the most significant technological breakthroughs in the Architecture, Engineering, and Construction (AEC) industry. The pace of implementation of BIM in AEC has increased during the past decade with an enhanced focus on sustainable construction. However, BIM implementation lags its potential because of several factors such as readiness issues, lack of previous experience in BIM, and lack of market demand for BIM. To evaluate and solve these issues, understanding the current BIM implementation in construction organizations is required. Motivated by this need, the main objective of this study is to propose a tool for the measurement of BIM implementation levels within an organization. Various sets of indexes are developed based on their pertinent Critical Success Factors (CSFs). A detailed literature review followed by a questionnaire survey involving 99 respondents is conducted, and results are analyzed to formulate a BIMp-Chart to calculate and visualize the BIM implementation level of an organization. Subsequently, the applicability of the BIMp-Chart is assessed by comparing and analyzing datasets of four organizations from different regions, including Qatar, Portugal, and Egypt, and a multinational organization to develop a global measurement tool. Through measuring and comparing BIM implementation levels, the BIMp-Chart can help the practitioners identify the implementation areas in an organization for proper BIM implementation. This study helps understand the fundamental elements of BIM implementation and provides a decision support system for construction organizations to devise proper strategies for the effectual management of the BIM implementation process.

Privacy Preservation in Resource-Constrained IoT Devices Using Blockchain—A Survey

With opportunities brought by Internet of Things (IoT), it is quite a challenge to assure privacy preservation when a huge number of resource-constrained distributed devices is involved. Blockchain has become popular for its benefits, including decentralization, persistence, immutability, auditability and consensus. With the implementation of blockchain in IoT, the benefits provided by blockchain can be derived in order to make IoT more efficient and maintain trust. In this paper, we discuss some applications of IoT in different fields and privacy-related issues faced by IoT in resource-constrained devices. We discuss some applications of blockchain in vast majority of areas, and the opportunities it brings to resolve IoT privacy limitations. We, then, survey different researches based on the implementation of blockchain in IoT. The goal of this paper is to survey recent researches based on the implementation of blockchain in IoT for privacy preservation. After analyzing the recent solutions, we see that the blockchain is an optimal way for preventing identity disclosure, monitoring, and providing tracking in IoT.

Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach

In real estate, there are various variables for the forecasting of future land prices, in addition to the macro and micro perspectives used in the current research. Examples of such variables are the economic growth rate, unemployment rate, regional development and important locations, and transportation. Therefore, in this paper, data on real estate and national price fluctuation rates were used to predict the ways in which future land prices will fluctuate, and macro and micro perspective variables were actively utilized in order to conduct land analysis based on Big Data analysis. We sought to understand what kinds of variables directly affect the fluctuation of the land, and to use this for future land price analysis. In addition to the two variables mentioned above, the factor of the landscape was also confirmed to be closely related to the real estate market. Therefore, in order to check the correlation between the landscape and the real estate market, we will examine the factors which change the land price in the landscape district, and then discuss how the landscape and real estate can interact. As a result, re-explaining the previous contents, the future land price is predicted by actively utilizing macro and micro variables in real estate land price prediction. Through this method, we want to increase the accuracy of the real estate market, which is difficult to predict, and we hope that it will be useful in the real estate market in the future.