The reliability and sustainability of civil infrastructure depend on high-resolution subsurface characterization, yet conventional site investigation is often limited by labor-intensive fieldwork and sparse measurements. This critical systematic review links the digital transformation of geological surveying to geotechnical site characterization, treating remote sensing, geophysics, and digital geological mapping as upstream evidence that constrains engineering interpretation. We synthesize how multi-source observations are converted into engineering-ready site models and decision-facing uncertainty for design and risk management. Through analyzing the intellectual structure of the field, we identify a shift toward integrated workflows that combine multi-platform sensing with physics-informed and explainable AI. We discuss where these workflows are mature enough to support engineering decisions (e.g. ground model updating, hazard screening for slopes, and construction-phase monitoring), and where they remain limited by transferability, registration errors, and validation constraints. The analysis reveals that the frontier is shifting beyond data acquisition toward four-dimensional geological digital twins that can be updated through monitoring and consumed within BIM-centered delivery. This review offers a roadmap for integrating smart sensing and AI into routine engineering practice, highlighting the necessity of explainable algorithms to ensure safety and resilience in the built environment.

Extreme climate events are becoming increasingly frequent, and flood disasters have been one of the most frequent and devastating forms of such events, threatening the lives and property of coastal residents. To reduce the potential costs to residential lives and property, fast and reasonable predictions and decisions should be made for quick emergency response based on timely flood routing analysis. This study proposes a hybrid model that aims to achieve real-time forecasting of time-varying flood routing and inundation maps by integrating hydrodynamic analysis and deep learning. A computational fluid dynamics (CFD) database of 125 simulated flood scenarios is established under varying flood frequency and runoff roughness of potential routing areas. Various deep learning networks, such as the long short-term memory (LSTM) network, convolutional neural network (CNN), and transpose convolutional neural network (TCNN), are used in this study to develop the proposed hybrid model for real-time and visual flood routing analysis. The results show that the model can quickly generate flood inundation maps with the input of real-time water level monitoring histories along the drainage basin, which provides valid support for emergency decision-making in various flood scenarios.

The construction industry requires an automated safety monitoring system to detect various objects during operations, thus preventing accidents caused by workers and large equipment. Utilizing LiDAR for monitoring, which can provide accurate 3D point cloud data, offers a promising solution. However, the objects involved in the safety monitoring have extremely different scales, such as large-scale excavators and small-scale workers, which pose challenges for detection with LiDAR. This study proposes a deep learning model that simultaneously detects complex multi-scale objects from LiDAR point clouds to address this issue. The proposed model generates object detection boxes using a voxel-based 3D sparse convolutional neural network and an anchor-free detection head based on candidate voxels. To improve the limited receptive field for detecting large-scale objects, additional feature extraction layers and improved large kernel convolution operations are incorporated into the network. A network with multi-scale 3D features and feature fusion from different scales is employed, enhancing the capability to detect objects of various sizes. The proposed method was trained and evaluated on the LiDAR data set we created. It achieves detection mAP exceeding 90% for both small-scale workers and large-scale excavators, indicating good multi-scale detection capability.

Continuous sign language recognition seeks to identify unsegmented sign language from videos by means of a weakly supervised manner, providing only sentence-level labels. In sign language videos, the gestures are smooth and continuous, and the same word may also correspond to video clips of different scales. Therefore, this poses a challenge in accurately capturing complex temporal dependencies. For hearing-impaired service robots, continuous sign language recognition capability is particularly critical, as the robots need to understand the natural sign language expressions of hearing-impaired users in real time. Previous studies have shown that using methods with a time-invariant receptive field for temporal modeling can partially address this issue, but they are not well-suited to handle video clips of varying scales. In this study, we re-examined the temporal modeling schemes in recent CSLR works and proposed the Two-stage Temporal ConvTransformer (T2CT), which fully leverages the advantages of one-dimensional convolutional neural networks and Transformer encoders, adopting a two-stage structure to capture more comprehensive spatiotemporal features. In particular, each stage of the proposed T2CT consists of two parts: a Local Temporal Modeling Module to capture short-term temporal dependencies, and a Global Temporal Modeling Module for long-term temporal modeling. Experimental results on three challenging CSLR datasets demonstrate that the proposed T2CT achieves competitive performance.

Server deployment is a critical and extensively researched issue in Mobile Edge Computing (MEC). As user tasks become increasingly diverse, existing studies on homogeneous edge server deployment can hardly meet practical demands, which may lead to poor Quality of Service (QoS), user satisfaction, and resource utilization. This paper investigates the problem of heterogeneous edge server deployment with task classification in MEC scenarios from the perspective of Edge Service Providers (ESPs). The deployment problem is formulated as a constrained optimization problem and decomposed into two subproblems. To address this challenge, an iteration-based Heterogeneous Edge Server Deployment and Offloading Algorithm (HEDOA) is proposed, which jointly optimizes server deployment, task offloading, and task assignment policies to minimize the total cost for ESPs. Experimental results on the Shanghai Telecom Dataset demonstrate HEDOA’s effectiveness and robustness.

Smart construction sites (SCSs) haven't been widely and effectively implemented given the interest conflicts of multi-stakeholders. The purpose is to explore the governance mechanism of SCSs under multi-stakeholder interactions to provide countermeasures for promoting SCSs. Speculative punishment and reputational incentives were creatively introduced to discuss their influences on the behaviors of SCS stakeholder, and the synergy of the government and the market mechanism in the SCS development is discussed. First, a four-subject evolutionary game model including the government, project owners, contractors and technology providers was established. Second, the governance processes of SCSs were analyzed according to evolutionary stable strategies (ESSs). Finally, the influences of parameter variations on players' strategies were illustrated by numerical simulations and further proposed the governance mechanism of SCSs. This study presents some findings: (1) Project owners are more sensitive to the government's incentives than other stakeholders. And the increased cooperation benefits for project owners and contractors are beneficial for the SCS development, while the externalities of SCSs easily result in project owners' free riding. (2) Speculative punishment improves the implementation effectiveness but hinders the adoption of SCSs. Reputational incentives and losses are beneficial for increasing stakeholders' enthusiasm. (3) The governance processes of SCSs are divided into four stages where the government and the market mechanism play different roles. Policy recommendations for the government and strategic references for enterprises are offered based on research findings.

In recent years, floods have brought renewed attention and requirement for real-time and city-scaled flood forecasting due to climate change and urbanization. In this study, a rapid assessment method for flood risk mapping is proposed by integrating aerial point clouds and deep learning technique that is capable of superior modeling efficiency and analysis accuracy for flood risk mapping. The method includes four application modules, i.e., data acquisition and preprocessing by oblique photography, large-scale point clouds segmentation by RandLA-Net, high-precision digital elevation model (DEM) reconstruction by modified hierarchical smoothing filtering algorithm, and hydrodynamics simulation based on hydrodynamics. To demonstrate the advantages of the proposed rapid assessment method more clearly, a case study is conducted in a local area of the South-to-North Water Transfer Project in China. The proposed method achieved 70.85% in mean intersection over union ( mIoU ) and 88.70% in overall accuracy ( OAcc ), outperforming the PointNet and PointNet++ networks. For the case point cloud containing nearly 50 million points, the computation time is less than 9 min, while the computation times for PointNet and PointNet++ are both more than 24 h. Then, high-precision DEM reconstruction by proposed hierarchical smoothing method with topographic feature embedding. These results demonstrate the efficiency and accuracy of the proposed method in processing large-scale 3D point clouds and rapid assessment of flood risk, providing a new perspective and effective solution for flood risk mapping in the field of spatial information science.

In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.

Coastal cities are vulnerable to typhoon disasters due to their unique geographical location. Although significant progress has been made in the safety early warning system for construction sites, there are few studies on site safety management under typhoon disasters. Moreover, traditional manual methods of on-site emergency management are lagging behind and rigid, making it difficult to respond quickly to safety inspections manually. This study aims to automatically identify safety hazards and provide effective response measures during typhoon warnings, ultimately enhancing the safety of construction sites. Therefore, based on the MATLAB platform, this study developed a rapid safety inspection system (RSS-Typh) for the construction site during the typhoon warning period. In addition, the standard operating procedures (SOP) related to the system are formulated to assist the comprehensive safety inspection of the site before the typhoon and improve the inspection efficiency. The specific operation can be divided into three steps: 1.Using a combination of drones and handheld laser scanners to complete the complete construction site data acquisition in less than 2 h, and complete the modeling and refinement of the point cloud model in 5 h; 2.Using the developed hidden danger detection algorithm based on automatic point cloud to effectively identify the deformation-related hidden dangers at the construction site; 3.Use the designed automatic query security countermeasure GUI system to make rapid rectification. This study assists site managers in swiftly implementing effective safety management on construction sites, elevating the safety level of the site before typhoon. Furthermore, it serves to advance the automation of safety management in construction sites, promoting further developments in this field.

Objectives. This systematic review aims to report the evaluation of wearable biosensors for the real-time measurement of stress and fatigue using sweat biomarkers. Methods. A thorough search of the literature was carried out in databases such as PubMed, Web of Science and IEEE. A three-step approach for selecting research articles was developed and implemented. Results. Based on a systematic search, a total of 17 articles were included in this review. Lactate, cortisol, glucose and electrolytes were identified as sweat biomarkers. Sweat-based biomarkers are frequently monitored in real time using potentiometric and amperometric biosensors. Wearable biosensors such as an epidermal patch or a sweatband have been widely validated in scientific literature. Conclusions. Sweat is an important biofluid for monitoring general health, including stress and fatigue. It is becoming increasingly common to use biosensors that can measure a wide range of sweat biomarkers to detect fatigue during high-intensity work. Even though wearable biosensors have been validated for monitoring various sweat biomarkers, such biomarkers can only be used to assess stress and fatigue indirectly. In general, this study may serve as a driving force for academics and practitioners to broaden the use of wearable biosensors for the real-time assessment of stress and fatigue.

This paper proposes an efficient method for quantifying the stratigraphic uncertainties and modeling the geological formations based on boreholes. Two Markov chains are used to describe the soil transitions along different directions, and the transition probability matrices (TPMs) of the Markov chains are analytically expressed by copulas. This copula expression is efficient since it can represent a large TPM by a few unknown parameters. Due to the analytical expression of the TPMs, the likelihood function of the Markov chain model is given in an explicit form. The estimation of the TPMs is then re-casted as a multi-objective constrained optimization problem that aims to maximize the likelihoods of two independent Markov chains subject to a set of parameter constraints. Unlike the method which determines the TPMs by counting the number of transitions between soil types, the proposed method is more statistically sound. Moreover, a random path sampling method is presented to avoid the directional effect problem in simulations. The soil type at a location is inferred from its nearest known neighbors along the cardinal directions. A general form of the conditional probability, based on Pickard's theorem and Bayes rule, is presented for the soil type generation. The proposed stratigraphic characterization and simulation method is applied to real borehole data collected from a construction site in Wuhan, China. It is illustrated that the proposed method is accurate in prediction and does not show an inclination during simulation.

Development and evaluation of a low-cost passive wearable exoskeleton system for improving safety and health performance of construction workers: A pilot study Shahnawaz Anwer , Heng Li , Mohammed Abdul-Rahman , Maxwell Fordjour Antwi-Afari Pages 164-171 (2023 Proceedings of the 40th ISARC, Chennai, India, ISBN 978-0-6458322-0-4, ISSN 2413-5844) Abstract: Construction workers have an increased risk of having muscle fatigue and musculoskeletal injuries, among other non-fatal workplace injuries. As a result, this project aimed to develop and evaluate a low-cost passive wearable exoskeleton system for improving construction workers' safety and health performance, mainly by mitigating the risk of developing musculoskeletal pain and fatigue. Surface electromyography (sEMG) was used to evaluate muscle activity in the Thoracic Erector Spinae (TES) and Lumbar Erector Spinae (LES) at the L3 and T12 vertebrae level, respectively, during repetitive handling tasks. In addition, both subjective (e.g., rating of the fatigue scale) and objective fatigue indicators (e.g., heart rate, skin temperature) were employed to assess fatigue. Exoskeleton use was associated with a 30% decrease in LES muscle activation compared to baseline. The application of an exoskeleton had a similar effect on the TES, decreasing muscle activity by 12%. When using an exoskeleton, a participant's neck kinematics were reduced by 23%, their low back kinematics by 11%, their hip kinematics by 5%, and their knee kinematics by 36%. Exoskeleton use was associated with a 13% decrease in heart rate and a 67% decrease in perceived fatigue. Nonetheless, skin temperature was raised by around 2% while using an exoskeleton compared to when not using one. Our preliminary findings suggest that the passive exoskeleton system could be an effective ergonomic intervention tool for assisting construction workers engaged in manual repetitive handling activities. Keywords: Construction safety, Exoskeleton device, Fatigue, Musculoskeletal injury DOI: https://doi.org/10.22260/ISARC2023/0024 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley Presentation Video: https://youtu.be/UoU3BlOY_4I

Adhering to green and low-carbon development is the only way to achieve sustainable economic development in China. High-speed railway (HSR) has the advantages of low energy consumption and low pollution, which is representative of the high-quality development of China's transportation industry. Based on the panel data of 285 cities in China from 2004 to 2017, this paper explores the impact of HSR development on CO2 emissions using a difference-in-difference model. The results show that the opening of HSR can effectively reduce the level of urban CO2 emissions. After the endogeneity test and the robustness test, this core conclusion is still valid. The mechanism test confirms that the opening of HSR can reduce the level of urban carbon emissions by promoting the upgrading of industrial structures and improving the level of green technology innovation. Based on empirical results, this paper proposes that we should make rational use of the positive externalities of the HSR network to promote green development, intensive development, and innovative development.

For multi-energy storage vehicles, the performance of online predictive energy management strategies largely relies on the length and effective utilization of predictive information. In this context, this paper proposes a novel velocity prediction method for the full driving cycle of electric vehicles based on the spatial–temporal commuting data, then the predicted velocity is applied to predictive energy management in electric vehicles with battery/supercapacitor hybrid energy storage system. Firstly, an one-year real-world commuting data set is collected on a Chinese arterial road with 10 intersections, 225 records are classified into 79 categories. Then, a real-time two-stage full driving cycle prediction method is proposed, where a medium-term prediction based on a long–short term memory (LSTM) network and a long-term prediction generated by a spatial–temporal interpolation method (STIM) are spliced for each category. The most probable category, i.e., the executed LSTM and STIM can be updated in real-time. Finally, a multi-horizon model predictive control method (MH-MPC) is established to leverage the predicted velocity for optimal power distribution. Compared with the conventional short-sighted MPC, the MH-MPC can reduce 4.2% battery degradation cost in a statistics form with real-time computation requirements satisfied.

Abstract In recent years, floods have brought renewed attention and requirement for real-time and city-scaled flood forecasting, due to climate change and urbanization. Flood risk mapping through traditional physics-based modeling methods is often unrealistic for rapid emergency response requirements, because of long model runtime, hydrological information lacking, and terrain change caused by human activity. In this study, an automated simulation framework is proposed by integrating aerial point clouds and deep learning technique that is capable of superior modeling efficiency and analysis accuracy for flood risk mapping. The framework includes four application modules, i.e., data acquisition and preprocessing, point clouds segmentation, digital elevation model (DEM) reconstruction, and hydrodynamics simulation. To more clearly demonstrate the advantages of the proposed automated simulation framework, a case study is conducted in a local area of the South-to-North Water Transfer Project in China. In addition, the efficiency and accuracy of the suggested point cloud segmentation network for large-scale 3D point clouds in basin scenes are discussed in detail by comparison with PointNet and PointNet + + networks.

The rapid growth of municipal solid waste put pressure on the end treatment facilities, and the site selection of MSW treatment facilities needs to be carefully conducted. This study aims to develop a Standard Operational Procedures (SOP) for municipal solid waste (MSW) treatment station site selection based on GIS and system dynamics combination. System dynamics was used to establish a forecasting model for MSW simulation followed by the spatial analysis method to generate a geographic information system. The MSW simulation in Xiamen city using the system dynamics model proved that the model was with high prediction accuracy. An SOP for GIS-based MSW treatment station site selection was developed. The site selection for MSW comprehensive treatment in Xiamen using GIS and system dynamics screened the locations of the comprehensive solid waste treatment stations in Xiamen and eliminated unsuitable lands using the mapping function of the geographic information system, thus validating the SOP.

Typhoon is one of the most important natural disasters in the coastal areas of China and caused severe economic losses every year. A typhoon disaster early warning evaluation system for historical buildings is established, and the influence of risk factors is analyzed using the analytic hierarchy process. The corresponding prevention strategies are proposed to reference the disaster prevention and protection for historical buildings under typhoon. Theoretically, this paper uses the algorithm advantages of the BP neural network algorithm to achieve the purpose of early warning of typhoon disaster risk. It constructs a historical building typhoon warning model based on BP neural network to evaluate the risk level of historical buildings. The risk early warning model is found to have some validity and reliability by training the neural network with sample data and comparing the performance data with the predicted data.

Aiming at the optimal cost planning problem in the process of raw material ordering and transshipment, this pa-per studies and develops a supply chain planning manage-ment model based on genetic algorithm by constructing ob-jective function and constraints. On the basis of analyzing the raw material demand of production enterprises, combined with the supplier's supply capacity and the transshipment loss rate of the transshipment carrier, the mathematical model of the optimal ordering scheme and the transshipment scheme is constructed, and the optimal supply planning model that can meet the actual supply chain planning needs is proposed, and the planning model is improved by using genetic algorithms, which greatly improves the operation speed and has good applicability.

Virtual reality (VR) technology is a kind of computer simulation system that can create and experience the virtual world. It uses computers to generate a simulation environment. It is a system simulation of multisource information fusion, interactive three-dimensional dynamic view, and physical behavior for users to immerse in the environment. The practical application of virtual instructional models is developed to support civil engineering teaching, disciplines related to civil engineering processes, including classroom education and e-learning technologies. The virtual model can be interactively operated, allowing the teacher or student to monitor the physical evolution of the work and the progress of architectural activities inherent in it. This paper analyzes the development status of VR and its application in teaching research and describes the application of a VR teaching model to demonstrate the feasibility and advantages of VR teaching. The developed application can display the physical evolution of the task, detect the planned construction sequence, and view the shape details of each construction component. The use of VR technology in developing these instructional applications contributes to education by increasing the efficiency of the models to allow for the interactivity of each simulation task. Therefore, the new concept of VR technology applied in teaching mode brings a new perspective to civil engineering education.