Digital Technologies in Architecture, Engineering and Construction (AEC)

A major railway project is a complex, giant system with multi-party participation, one characterized by complex geological conditions, long construction periods and large scale, which leads to an increased likelihood of safety risk events during construction. In order to solve the problem of scientific selection and formulation of safety risk prevention and control strategies for major railway projects, an auxiliary selection method of safety risk prevention and control strategies for major railway projects based on weighted Euclidean distance (WED) is proposed. The relevant ontology is used to conceptualize and formalize the knowledge of safety risks of major railroad projects, and combine the characteristics of major railroad projects; it refers to the prevention and control measures of historical safety risk events associated with major railroad projects, and then constructs the knowledge structure and case base around safety risks of major railroad projects and the circumstances of the case. In determining the comprehensive weights, the G1 method is used to determine the subjective weights, the anti-entropy weight method is used to determine the objective weights and game theory combines the subjective and objective weights. In comparing the array of safety risk prevention and control cases associated with major railway projects, the weighted Euclidean distance is used to calculate the similarity between these cases and the target case, which in turn assists project managers in determining the safety risk prevention and control strategies appropriate for major railway projects. This study takes Landslide No. 1 in the Tunnel A inlet planning area as an example. It utilizes the WED method to assist in selecting safety risk prevention and control strategies for major railway projects, which verifies the method’s feasibility. The proposed method enriches the method of the assisted selection of safety risk prevention and control strategies for major railway projects, makes strategy formulation more scientific, has specific reference significance for the formulation of safety risk prevention and control strategies for major railway projects, and promotes the improvement of safety risk prevention and risk control for participating units. Full article

Road structures undergo a series of chemical and physical processes once they are put into service. This phenomenon results from the action of the load and the influence of the environment, which causes their progressive deterioration. In order to mitigate the risk of progressive deterioration and guarantee their stability and durability, various maintenance tasks are required, including visual inspections. The Intelligent Bridge Management System of Colombia (SIGP) includes visual inspection as one of its modules. The system has been designed based on state-of-the-art criteria and national experience with relevant damages and bridge collapses. This paper presents the visual inspection methodology, which includes several stages such as a classification scale, condition index, evaluation areas, damage catalog, and evaluation criteria. In addition, a digital application has been developed to facilitate real-time data collection during field inspections using mobile devices, which can be uploaded directly to the system database hosted in the cloud. The results from the inspection of bridges of different typologies and years of construction are presented, as well as general inspection results from 150 bridges in Colombia. The relevance, comprehensiveness, and accuracy of the inspection are supported by a damage catalog, which allows the identification of intervention needs and reduces the bias of the collected data. Full article

In the rapidly advancing field of construction, digital site management and Building Information Modeling (BIM) are pivotal. This study explores the integration of drone imagery into the digital construction site management process, aiming to create BIM models with enhanced object recognition capabilities. Initially, the research sought to achieve photorealistic rendering of point cloud models (PCMs) using blur/sharpen filters and generative adversarial network (GAN) models. However, these techniques did not fully meet the desired outcomes for photorealistic rendering. The research then shifted to investigating additional methods, such as fine-tuning object recognition algorithms with real-world datasets, to improve object recognition accuracy. The study’s findings present a nuanced understanding of the limitations and potential pathways for achieving photorealistic rendering in PCM, underscoring the complexity of the task and laying the groundwork for future innovations in this area. Although the study faced challenges in attaining the original goal of photorealistic rendering for object detection, it contributes valuable insights that may inform future research and technological development in digital construction site management. Full article

Due to the dynamic and complex nature of mega construction projects (MCPs), mega construction project risks (MCPRs) have significantly increased in recent years. Technological innovation cooperation (TIC) is accepted as an approach to solve these issues. However, considering the new technological innovation challenges, technological innovation risks (TIRs) have been identified as a limitation of TIC. This study aims to develop a conceptual framework to explain TIC for MCPs. It is based on a review of the literature, engineering practice, and logical reasoning. The conceptual framework describes the interaction between MCPRs and TIC. It points out that MCPRs drive technological innovation, and technological innovation objectives guide the TIC. TIC has a negative effect on solving TIRs, and TIRs positively affect MCPRs. Cooperation performance will mediate the relationship between TIC and MCPRs. The conceptual framework may provide a theoretical basis to guide future empirical studies that validate the relationship between MCPRs and TIC and puts forward reasonable suggestions for MCPs. Full article

Unmanned aerial vehicle (UAV) remote sensing technology is vigorously driving the development of digital cities. For experimental objects such as large, protruding, and structurally complex steel truss railway bridge structures, commonly used oblique photography and cross-circular photography techniques can lead to blurring, missing, or lower accuracy of fine texture in the models. Therefore, this paper proposes a real-scene three-dimensional modeling method that combines oblique photography with inclined photography and compares it with oblique photography and cross-circular photography techniques. Experimental results demonstrate that the model generated by combining oblique photography with inclined photography exhibits clearer textures, more complete lines, and higher accuracy, meeting the accuracy requirements of 1:500 topographic map control points. This method plays a beneficial auxiliary role in the inspection of ailments such as steel structure coating corrosion and high-strength bolt loss in steel truss railway arch bridges. Full article

With the rapid development of new construction methods, China’s construction industry is facing the transformation challenges of industrialization and informationization. However, migrant workers are characterized by high mobility, low education, and poor skills in China’s national conditions, which can no longer meet the requirements of operations. The transformation of the low-level manual migrant workers in the construction industry to high-level skilled industrial workers is inevitable. In order to explore how to better achieve the transformation of construction workers, evolutionary game research with construction unit and labor company as the subjects was carried out. Three types of assumptions were introduced into the constructed evolutionary game model: cooperation mechanism, spillover effect, and incentive mechanism (CSI). Simulation experiments and analysis of the model were finally conducted. The results of the game analysis finally show: (1) a higher initial proportion of selected transformed industrial workers; (2) a fair benefit concession from the construction unit to the labor company; (3) a lower revenue spillover effect; (4) that a higher level of regulation and incentives are conducive to the evolutionary game to converge to the desired state at a faster rate. The findings provide ideas for improving the labor system in China’s construction industry and lay the foundation for solving the labor specialization problem of new construction methods. Full article

To address the incomplete image data collection of close-to-ground structures, such as bridge piers and local features like the suspension cables in bridges, obtained from single unmanned aerial vehicle (UAV) oblique photography and the difficulty in acquiring point cloud data for the top structures of bridges using single terrestrial laser scanners (TLSs), as well as the lack of textural information in TLS point clouds, this study aims to establish a high-precision, complete, and realistic bridge model by integrating UAV image data and TLS point cloud data. Using a particular large-scale dual-track bridge as a case study, the methodology involves aerial surveys using a DJI Phantom 4 RTK for comprehensive image capture. We obtain 564 images circling the bridge arches, 508 images for orthorectification, and 491 images of close-range side views. Subsequently, all images, POS data, and ground control point information are imported into Context Capture 2023 software for aerial triangulation and multi-view image dense matching to generate dense point clouds of the bridge. Additionally, ground LiDAR scanning, involving the placement of six scanning stations both on and beneath the bridge, was conducted and the point cloud data from each station are registered in Trimble Business Center 5.5.2 software based on identical feature points. Noise point clouds are then removed using statistical filtering techniques. The integration of UAV image point clouds with TLS point clouds is achieved using the iterative closest point (ICP) algorithm, followed by the creation of a TIN model and texture mapping using Context Capture 2023 software. The effectiveness of the integrated modeling is verified by comparing the geometric accuracy and completeness of the images with those obtained from a single UAV image-based model. The integrated model is used to generate cross-sectional profiles of the dual-track bridge, with detailed annotations of boundary dimensions. Structural inspections reveal honeycomb surfaces and seepage in the bridge piers, as well as painted rust and cracks in the arch ribs. The geometric accuracy of the integrated model in the X, Y, and Z directions is 1.2 cm, 0.8 cm, and 0.9 cm, respectively, while the overall 3D model accuracy is 1.70 cm. This method provides technical reference for the reconstruction of three-dimensional point cloud bridge models. Through 3D reconstruction, railway operators can better monitor and assess the condition of bridge structures, promptly identifying potential defects and damages, thus enabling the adoption of necessary maintenance and repair measures to ensure the structural safety of the bridges. Full article

Due to increasing cost and decreasing labor, prefabricated buildings have developed rapidly. With the prolongation of prefabricated buildings’ supply chain (PBSC) and an increase in risk factors, project delays and even interruptions occur occasionally. The difficulty of supply chain management is increasing. Supply chain resilience (SCR) as a risk management tool has gradually attracted the attention of scholars. This paper uses the grounded theory to identify the influencing factors of prefabricated buildings’ supply chain resilience (PBSCR) based on the dynamic capacity theory. By collecting questionnaires from relevant stakeholders in Shenyang and Hangzhou, a structural equation model (SEM) was used to test the research hypothesis. The capacity effect relationship of the PBSC was constructed. The results show that resilient capability has the highest direct effect on the improvement in PBSCR, and collaborative capability has the highest total and indirect effect on the improvement in PBSCR. The critical paths to improving PBSCR were then identified. Suggestions were made based on the calculated effect relationships. This paper is expected to improve PBSCR, enrich the research on supply chains in the construction field, and help better realize the stable development of prefabricated buildings. Full article

During the digital transformation of construction projects, the significant volume of project data raise a multitude of data responsibility issues. Project stakeholders, often motivated by financial interests and other considerations, frequently engage in data fraud, namely the alienation of project digital responsibility (APDR), which ultimately hinders the benefits released by the digital transformation of projects. However, the causes of APDR are still unclear. This study aims to bridge this knowledge gap by empirically investigating the factors influencing APDR and delineating their pathways. A model outlining the mechanism of APDR formation, rooted in fraud risk factor theory (FRFT) and information asymmetry theory (IAT), is proposed. To collect data from 276 Chinese construction project practitioners, a questionnaire was meticulously designed. Confirmatory factor analysis (CFA) was subsequently applied to assess the validity of the proposed model. Finally, the proposed model consisting of six variables was examined using structural equation modeling (SEM). The results showed that opportunity (OPP), motivation (MOT), and information asymmetry (INF) had a positive effect on APDR, while exposure probability (EXP), penalty strength (PEN), and ethics (ETH) had a negative effect on APDR. Through revealing the formation mechanism of APDR, the findings are beneficial for understanding why stakeholders adopt APDR at the risk of being penalized. This study aims at deepening the systematic understanding of APDR and enriches the relevant theories on project digital responsibility (PDR). Such knowledge would also contribute to project managers proposing effective interventions to inhibit APDR and promote PDR. Full article

Bridges have a special place in transportation infrastructures and road networks due to their direct relationship with other places. These structures have the purpose of maintaining the traffic loads of the highway, crossing any obstacle, and performing effective communication between two destinations. Costs associated with bridge maintenance continue to be expensive due to their widespread use and stringent inspection requirements. Many researchers have been working on methods to use machine-learning (ML) techniques to forecast specific situations rather than physically checking bridges as part of the maintenance process in recent years. The practical value of the models has, however, been severely constrained by issues such relatively poor model evaluation results, unstable model performances, and the ambiguous application of established models in real-world scenarios. This work showed a thorough method of bridge condition prediction model building from feature engineering to model evaluation, along with a clear procedure of applying the produced model to actual usage, using data from the United States National Bridge Inventory (NBI) and the Adaboost algorithm. Multiple ML model assessment metrics’ findings revealed that the given model outperformed the majority of earlier studies in terms of values and stability. The case study demonstrated that there is a 30% reduction in the number of bridges that need to be inspected. This study serves as a crucial resource for the practical application of ML approaches in the forecast of the status of civil infrastructure. Additionally, it shows that boosted ML models may be a superior option as modeling algorithms advance. To explore the main influencing aspects of bridge conditions, a predictor importance analysis is also offered. Full article