Search results

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

The crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.

This paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.

The analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.

The paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.

The proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.

While several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

Based on the systematic literature review, this paper aims to propose a framework of Construction 4.0 (C4.0) scenarios, identifying Industry 4.0 (I4.0) enabling technologies and their applications in the construction industry. The paper reviews C4.0 trends and potential areas for development.

In this research, a systematic literature review (SLR) methodology has been applied, including bibliographic coupling analysis (BCA), co-citation network analysis of keywords, the content analysis with the visualisation of similarities (VOSviewer) software and aggregative thematic analysis (ATA). In total, 170 articles from the top 22 top construction journals in the Scopus database between 2013 and 2021 were analysed.

Six C4.0 scenarios of applications were identified. Out of nine I4.0 technology domains, Industrial Internet of Things (IIoT), Cloud Computing, Big Data and Analytics had the most references in C4.0 research, while applications of augmented/virtual reality, vertical and horizontal integration and autonomous robotics yet provide ample avenues for the future applied research. The C4.0 application scenarios include efficient energy usage, prefabricated construction, sustainability, safety and environmental management, indoor occupant comfort and efficient asset utilisation.

This research contributes to the body of knowledge by offering a framework of C4.0 scenarios revealing the status quo of research published in the top construction journals into I4.0 technology applications in the sector. The framework evaluates current C4.0 research trends and gaps in relation to nine I4.0 technology domains as compared with more advanced industry sectors and informs academic community, practitioners and strategic policymakers with interest in C4.0 trends.

For prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.

This paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.

This paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO.

With respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated components in an environment where all parties in the supply chain of prefabricated components participate to solve the production scheduling problem. In addition, this paper creatively applies the improved BBO to the production scheduling problem of prefabricated components. Compared to other algorithms, the results show that the improved BBO show optimized result.

The proposed approach helps prefabricated component manufacturers consider complex requirements which could be used to formulate a more scientific and reasonable production plan. The proposed plan could ensure the construction project schedule and balance the reasonable requirements of all parties. In addition, improving the ability of prefabricated component production enterprises to deal with uncertain events. According to actual production conditions (such as the occupation of mold resources and storage resources of completed components), prefabricated component manufacturers could adjust production plans to reduce the cost and improve the efficiency of the whole prefabricated construction project.

The value of this article is to provide details of the procedures and resource constraints from the perspective of the precast components supply chain, which is closer to the actual production process of prefabricated components. In addition, developing the production scheduling for lean production will be in line with the concept of sustainable development. The proposed lean production scheduling could establish relationships between prefabricated component factory manufacturers, transportation companies, on-site contractors and production workers to reduce the adverse effects of emergencies on the prefabricated component production process, and promote the smooth and efficient operation of construction projects.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

With increasing complexity of construction projects and new construction processes and methods are adopted, more safety hazards are emerging at construction sites, requiring the application of the modern risk management methods. As an emerging technology, digital twin has already made valuable contributions to safety risk management in many fields. Therefore, exploring the application of digital twin technology in construction safety risk management is of great significance. The purpose of this study is to explore the current research status and application potential of digital twin technology in construction safety risk management.

This study followed a four-stage literature processing approach as outlined in the systematic literature review procedure guidelines. It then combined the quantitative analysis tools and qualitative analysis methods to organize and summarize the current research status of digital twin technology in the field of construction safety risk management, analyze the application of digital twin technology in construction safety risk management and identify future research trends.

The research findings indicate that the application of digital twin technology in the field of construction safety risk management is still in its early stages. Based on the results of the literature analysis, this paper summarizes five aspects of digital twin technology's application in construction safety risk management: real-time monitoring and early warning, safety risk prediction and assessment, accident simulation and emergency response, safety risk management decision support and safety training and education. It also proposes future research trends based on the current research challenges.

This study provides valuable references for the extended application of digital twin technology and offers a new perspective and approach for modern construction safety risk management. It contributes to the enhancement of the theoretical framework for construction safety risk management and the improvement of on-site construction safety.

The construction of cement asphalt (CA) emulsified mortar can obviously disturb the slab status after the fine adjustment. To decrease or eliminate the influence of CA mortar grouting on track slab geometry status, the effects of grouting funnel, slab pressing method, mortar expansion ratio, seepage ratio and grouting area on China Railway Track System Type (CRTS I) track slab geometry status were discussed in this paper.

Combined with engineering practice, this paper studied the expansion law of filling layer mortar, the liquid level height of the filling funnel, the pressure plate device and the amount of exudation water and systematically analyzed the influence of filling layer mortar construction on the state of track slab. Relevant precautions and countermeasures were put forward.

The results showed that the track slab floating values of four corners were different with the CA mortar grouting and the track slab corner near CA mortar grouting hole had the maximum floating values. The anti-floating effect of “7” shaped slab pressing device was more efficient than fixed-joint angle iron, and the slab floating value could be further decreased by increasing the amount of “7” shaped slab pressing devices. After CA mortar grouting, the track slab floating pattern had a close correlation with the expansion rate and water seepage rate of CA mortar over time and the expansion and water seepage rate of the mortar were faster when the temperature was high. Furthermore, the use of strip CA mortar filling under the rail bearing platform on both sides could effectively reduce the float under the track slab, and it could also save mortar consumption and reduce costs.

This study plays an important role in controlling the floating values, CA mortar dosage and the building cost of projects by grouting CA mortar at two flanks of filling space. The research results have guiding significance for the design and construction of China's CRTS I, CRTS II and CRTS III track slab.

In recent years, cost overrun becomes a common problem in steel building construction projects. The average percentage can vary widely depending on the project type, size, complexity and location. The steel structure change ratio in Taiwan is from 1 to 18% in statistics. The contractors always put every possible effort into preventing or mitigating project cost overruns, and one of the approaches is an accurate cost overrun risk estimate. Traditional project cost overrun risk assessment models mainly focus on macro-level evaluation and may not function well for the project-specific level (micro-level). This study creates a network-like connection model between the outcome (i.e. cost overrun risk) and the associated root causes in which the project status evaluation checklists of design, manufacturing, construction and interfaces are used to evaluate the checklists' influences through the Bayesian network (BN) composed by intermediate causes.

Due to the constraint of data availability, BN nodes, relationships and conditional probabilities are defined to establish a BN-based steel building project cost overrun assessment model following the knowledge of experts. Because of the complexity of the BN, the construction of the BN structure is first to build BN's fault tree (FT) hierarchy. And then, basic BN framework is constructed by the transformation of the FT hierarchy. Furthermore, some worthwhile additional arcs among BN nodes are inserted if necessary. Furthermore, conditional probability tables (CPTs) among BN nodes are explored by experts following the concept of the ranked node. Finally, the BN-based model was validated against the final cost analysis reports of 15 steel building projects done in Taiwan and both were highly consistent. The overall BN-based model construction process consists of three steps: (1) FT construction and BN framework transformation, (2) CPT computation and (3) model validation.

This study established a network-like bridge model between the outcome (i.e. cost overrun risk) and the root causes in a network of which cost influences are evaluated through the project-specific status evaluation checklists of design, manufacturing, construction and interfaces. This study overcame several limitations of the previous cost overrun risk assessment models: (1) few past research support assessment of cost overrun based on real-time project-owned data and (2) the traditional causal models inadequately depict interdependencies among influence factors of cost overrun at the network. The main influence factors of the cost overrun risk at the steel building projects in Taiwan were also examined using sensitivity analysis. The main root causes of cost overrun in steel building projects are design management and interface integration.

The proposed model belongs to the project-specific causal assessment model using real-time project-owned status checklist data as input. Such a model was seldom surveyed in the past due to the complicated interdependence among causes in the network. For practical use, a convenient and simple regression equation was also developed to forecast the cost overrun risk of the steel building project based on the root causes as input. Based on the analysis of cost overrun risk and significant influence factors, proper tailor-made preventive strategies are established to reduce the occurrence of cost overrun at the project.

Digital twin (DT) and building information modeling (BIM) are interconnected in some ways. However, there has been some misconception about how DT differs from BIM. As a result, industry professionals reject DT even in BIM-based construction projects due to reluctance to innovate. Furthermore, researchers have repeatedly developed tools and techniques with the same goals using DT and BIM to assist practitioners in construction projects. Therefore, this study aims to assist industry professionals and researchers in understanding the relationship between DT and BIM and synthesize existing works on DT and BIM.

A systematic review was conducted on published articles related to DT and BIM. A total record of 54 journal articles were identified and analyzed.

The analysis of the selected journal articles revealed four types of relationships between DT and BIM: BIM is a subset of DT, DT is a subset of BIM, BIM is DT, and no relationship between BIM and DT. The existing research on DT and BIM in construction projects targets improvements in five areas: planning, design, construction, operations and maintenance, and decommissioning. In addition, several areas have emerged, such as developing geo-referencing approaches for infrastructure projects, applying the proposed methodology to other construction geometries and creating 3D visualization using color schemes.

This study contributed to the existing body of knowledge by overviewing existing research related to DT and BIM in construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research.