Construction Safety Risk Research Articles

Quality Risk Evaluation of Urban Rail Transit Construction Based on AHP–FCE Method

The demand for urban transport is increasing globally, and urban rail transit is an important infrastructure for meeting this demand. The objectives of this study were to effectively control and prevent all types of risks in the construction of metro projects and improve the quality and safety control of urban metro project construction. First, 20 index factors were selected from the five dimensions of “man–machinery–materials–methods–environment” and constructed an index system for assessing urban metro construction quality risks. Second, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were used to comprehensively evaluate the construction quality risks of subway projects, and the weights of the secondary indices were determined. Finally, the importance of secondary indicators was evaluated using the integrated AHP–FCE method, and the model was applied to engineering practice for validation. The results indicated that the comprehensive AHP–FCE method has good adaptability and rationality and has practical application value for metro project construction quality and safety risk assessment. It can help prevent urban metro construction quality accidents and provides a novel idea for metro project construction quality risk assessment.

Developing a cloud evidence method for dynamic early warning of tunnel construction safety risk in undersea environment

Traditional methods have limitations in achieving precise predictions of risk occurrence at an exact future time and have difficulties transforming between qualitative and quantitative indicators and handling multi-source heterogeneous risk data. This study quantifies and analyzes the multi-source construction safety risks classified into the categories of man, machine, material, method and environment (4M1E), and presents a cloud evidence method that integrates wavelet de-noising algorithm, cloud model, and Dempster-Shafer (D-S) evidence theory. A real-time risk prediction and warning is provided using this method after the fusion of multi-source uncertain information and the transformation between qualitative and quantitative indicators, enabling the timely detection of potential risks for project managers. This method analyzing “uncertainty” with “certainty” is verified by an undersea tunnel construction project. The result shows that this method is effective in early warning risks two days before their actual occurrence, providing reference significance for risk early warning of the tunnel construction project.

Emergency Decision Making for Electric Power Personal Accidents Based on Ontology and Case-Based Reasoning

Improvements in the emergency response efficiency and management level of power construction sites are conducive to reducing the construction safety risk of power infrastructure projects and then achieving the sustainability of construction site safety. Therefore, this paper proposes an emergency decision-making method for electric power personal accidents, which applies ontology and case-based reasoning to electric power emergency decision making. Firstly, ontology technology is used to structurally represent power accident case knowledge and clarify concepts and their relationships. Then, a power accident knowledge ontology hierarchy is designed, and a powerful personal accident case library is established. Secondly, by calculating cases’ conceptual similarity, attribute similarity, and structural similarity, a global power accident case similarity calculation method is proposed, and case matching is performed based on the calculation results to achieve case knowledge retrieval and reuse. Finally, the results of the example-based study show that the method effectively achieves the accurate retrieval of electric power accident cases, improves the efficiency of the emergency decision response to electric power construction site accidents, and then provides support for emergency decision making for electric power construction site accidents.

The Impact of Wearable Devices on the Construction Safety of Building Workers: A Systematic Review

Worker safety is a key concern in the construction industry. Making construction safer by reducing safety hazards is critical to sustainably developing the construction industry. Big data, the Internet of Things, artificial intelligence, and other emerging information technologies are deeply integrated within the construction industry. The real-time monitoring of important physiological indicators of the physical state of construction workers is possible with the use of wearable sensing equipment and can pre-emptively give warning of safety hazards. Data mining and analysis of the monitoring data enable an assessment of the safety status of construction workers and can thus reduce potential hazards faced by construction workers on site. This study firstly reviewed the applications of common wearable devices in various industries, focusing on their use in construction safety. Then, CiteSpace 6.1 R4 software was used to visually analyze the literature data related to wearable devices in construction safety. Thirdly, a comprehensive review was conducted on the psychological state and safety evaluation methods of construction workers. Finally, several challenges and future research trends of wearable devices in the construction safety field were discussed. This paper has important theoretical value in advancing the field of construction safety risk management and improving risk control strategies.

Knowledge Graph Improved Dynamic Risk Analysis Method for Behavior-Based Safety Management on a Construction Site

Construction sites are considered high-risk working environments. Safety risk analysis of construction sites is one of the important aspects of construction safety management. However, existing construction safety risk analysis methods typically face overreliance on subjective experience and cannot reflect the real-time risk level of construction projects. Crucial information about construction accidents implies a complex semantic network that can obtain objective quantitative data and then serve the quantitative analysis of construction safety risk. Therefore, a knowledge-graph-improved dynamic risk analysis method for behavior-based safety (BBS) management on construction sites is proposed. Specifically, this study quantifies the risks and consequences of unsafe behaviors in construction by conducting graph topology analysis based on historical accident data, improves the grey clustering model, and calculates the construction site risk. Thus, the proposed method combines expert experience and objective historical data to give more objective and realistic results. The results of the application of this method on one project show that the improved dynamic analysis method can utilize the historical accident data to achieve a more reasonable overall risk grading for BBS on construction sites; at the same time, the method can determine real-time key BBS indicators; further, the construction safety management measures for key BBS indicators can effectively reduce current BBS risks of a construction project.

Study on the Division of the Affected Zone under Construction Unloading and Its Construction Sequence of the Multiline Parallel River-Crossing Pipe Jacking

There are many advantages of reasonably determining the affected zone and its construction sequence for the multiline parallel pipe-jacking construction, such as convenient to reasonably arrange the pipeline section distribution mode, reducing the construction safety risk, and construction difficulty. Combined with the engineering construction practice of the multiline parallel pipe-jacking project for the north city drainage and flood control project through Chu River in Hefei, the strength reduction method has been utilized to study the surrounding stratum FOS for the pipe-jacking construction unloading under different overburden thickness Hs, different clear distance D, and different river water depth H w in this paper. The formula of the minimum critical overburden thickness Hsmin and the minimum critical clear distance Dmin of the surrounding stratum self-stability during pipe-jacking construction unloading have been derived. The division method of the affected zone for the multiline parallel river-crossing pipe-jacking construction which behaves as the mutual influence nonself-stability zone, the mutual influence self-stability zone, and the no mutual influence self-stability zone has been proposed. For further discussion, the construction sequence of the multiline parallel river-crossing pipe jacking has been studied.

Construction Technology and Safety Risk Control Measures of Deep Foundation Pit Excavation

Deep foundation pit excavation is a basic and key step involved in modern building construction. In order to ensure the construction quality and safety of deep foundation pits, this paper takes a project as an example to analyze deep foundation pit excavation technology, including the nature of this construction project, the main technical measures in the construction of deep foundation pit, and the analysis of the safety risk prevention and control measures. The purpose of this analysis is to provide scientific reference for the construction quality and safety of deep foundation pits.

Coupling Analysis of Tunnel Construction Safety Risks Based on N-K Model and SD Causality Diagram

Tunnel construction is characterized by its large scale, long periods and vulnerability to environmental impact, which pose great challenges to tunnel construction safety. In order to analyze the coupling mechanism of tunnel construction safety risks and assess these risks, we conducted a study on the coupling evaluation of these risks in order to improve tunnel construction safety risk management. By analyzing 150 accident cases related to tunnel construction safety, an N-K model (natural killing model) was constructed to quantify the risk level of each coupling form from four aspects—personnel risk factors, equipment risk factors, environmental risk factors and management risk factors—and the SD (system dynamics) causality diagram was used to construct risk element conduction paths and identify the key influencing factors of different coupling forms. The research results show that with the increase in risk coupling factors, the risk of tunnel construction safety accidents also increases; weak personnel safety awareness, aging and wear of equipment, poor operating environment and construction site management chaos are the key risk factors whose prevention needs to be focused on. The related research results can provide a new method for decision makers to assess tunnel construction safety risks and enrich the research on tunnel construction safety risk management.

Dynamic assessment and system dynamics simulation of safety risk in whole life cycle of coal mine.

Based on the accident causation theory, the mechanism and inducement of construction safety risk in mining enterprises are clarified. The safety risk system of mining enterprises is divided into five subsystems: personnel, material equipment, technology, environment, and management by rough set theory. The comprehensive weight of each risk factor is calculated by network analytic hierarchy process and fuzzy comprehensive evaluation method. Taking a mine in Shanxi Province as the research object, the causal traceability diagram and stock flow diagram of the risk system of mining enterprises are constructed by means of system dynamics model. The influence of various risk factors of the mine on the overall safety risk management level of the enterprise is simulated, and the numerical value of key personnel influence factors is adjusted. The sensitivity changes of safety productivity and safety risk management level of mining enterprises in different situations are analyzed. The results show that: (1) the management and personnel subsystem has the greatest impact on the safety risk management of mining enterprises, followed by the technology, material equipment, and environment subsystem. (2) Increasing safety input can improve the safety level and reduce the expected safety value time, otherwise it will reduce the safety level and delay the expected safety value time. (3) Further simulation of the personnel subsystem, it is found that the factors affecting the safety level of mining enterprises contain six factors, namely, the technical level of construction personnel, the management level of manager, the conduct code of construction personnel, the safety consciousness of practitioners, the basic quality of construction personnel, and the physical and mental state of construction personnel. (4) The conversion rate of personnel safety input to manager's management level and the safety consciousness of practitioners presents a steep decline-slow rise-gradually steady development trend, which mainly because the benefits of safety input have certain time delay and lag.

Using cased based reasoning for automated safety risk management in construction industry

The purpose of this study is to develop a CBR-based platform that integrates all steps of safety risk management (SRM) for the realization of automation in construction safety risk management. To realize this purpose, accident data from 2015 to 2020 in China has been collected and analyzed. Based on the collected data, this study adopts system thinking and stakeholder theory to establish an accident attribute system for accurate and comprehensive case representation, which was usually ignored in the previous study. Following case representation, the development process of case-based reasoning (CBR) is introduced in detail. The core of CBR—case retrieval is designed with the k-Nearest Neighbor (k-NN) algorithm and rough set theory to improve retrieval accuracy. In terms of case reuse and revise, a hybrid risk response model based on historical experience and risk factor control is proposed. Finally, the practical application of this study is illustrated with a specific construction project. The findings of this study confirm the feasibility of machine learning in improving safety performance at construction sites. Construction accidents can be effectively prevented by identifying the potential safety risks, and employing on-site management.

Construction safety risk assessment method of construction engineering based on improved SVM

Construction safety risk assessment method of construction engineering based on improved SVM

Construction safety risk assessment method of construction engineering based on improved SVM

Construction safety risk assessment method of construction engineering based on improved SVM

BIM-Integrated Construction Safety Risk Assessment at the Design Stage of Building Projects

The construction industry has a higher rate of work accidents than other industries. As a proactive approach to safety management, Construction Hazard Prevention through Design (CHPtD) can significantly eliminate or reduce construction safety risks. This method consists of three indices namely Likelihood, Consequence and Exposure. Likelihood and consequence index of 19 construction jobs using 2018 injury and death data. Only 4 types of work are shown in this case. The findings conclude that the index of relative mortality frequency (RFI) of roofers (47-2180) and the index of relative frequency of day-going injuries (RDAIFI) of construction workers (47-2060) are relatively high. In addition, construction equipment operators relative day-to-day injury severity (RDAIS) rates (47–2070) are 3.3 times that of roof operators (47–2180). These results validate that using plug-ins in Revit can quickly calculate the construction safety risks of various design plans in a very short time and show that the plug-ins developed have very good engineering application value.

Research on Construction Safety Risk of Prefabricated Construction Project

With the development of the construction industry and the vigorous promotion of national policies, prefabricated buildings have developed rapidly. Compared with traditional buildings, prefabricated buildings have the characteristics of fast construction, low cost and low pollution. The construction of prefabricated buildings has higher requirements for constructors and is more difficult to obtain professional skills. In particular, safety accidents are prone to occur during the hoisting of prefabricated components, resulting in casualties and economic losses. The safety risk in the construction of prefabricated buildings has become an urgent problem to be solved. This paper focuses on the safety risk analysis from the four aspects of manufacturing, transportation, storage and on-site assembly of prefabricated components in the construction process of prefabricated buildings, and gives the corresponding safety risk prevention measures.

Research on the construction safety risk assessment of prefabricated subway stations in China

PurposePrefabricated 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.Design/methodology/approachA 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.FindingsThe 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.Originality/valueThis 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.

Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach

Construction risk assessment (RA) based on expert knowledge and experience incorporates uncertainties that reduce its accuracy and effectiveness in implementing countermeasures. To support the construction of RA procedures and enhance associated decision-making processes, machine learning (ML) approaches have recently been investigated in the literature. Most ML approaches have difficulty processing dependency information from real-life construction datasets. This study developed a novel RA model that incorporates a graph convolutional network (GCN) to account for dependency information between construction accidents. For this purpose, the construction accident dataset was restructured into an accident network, wherein the accidents were connected based on the shared project type. The GCN decodes the construction accident network information to predict each construction activity’s severity outcome, resulting in a prediction accuracy of 94%. Compared with the benchmark feedforward network (FFN) model, the GCN demonstrated a higher prediction accuracy and better generalization ability. The developed GCN severity predictor allows construction professionals to identify high-risk construction accident scenarios while considering dependency based on the shared project type. Ultimately, understanding the relational information between construction accidents increases the representativeness of RA severity predictors, enriches ML models’ comprehension, and results in a more reliable safety model for construction professionals.

Nexus between Building Information Modeling and Internet of Things in the Construction Industries

The process of integrating building information modeling (BIM) and Internet of Things (IoT)-based data sources is a recent development. As a generalization, BIM and IoT data provide complementary perspectives on the project that complement each other’s constraints. Applying the concept of BIM-IoT in the construction industries which has been termed to have a high-risk factor could offer an improvement in the overall performance of the construction industries and thereby reduce the associated risks. This study aims to examine the potential of integrating BIM-IoTs in the construction industries by examining related published literature. Literature analysis revealed that the BIM and IoT have been extensively applied individually to several aspects of construction projects such as construction safety risk assessment, construction conflict management, building construction sustainability, and onsite construction process monitoring. However, there is scanty research awareness on the possibilities of BIM-IoT integration in the construction industries.

Application of Ensemble Machine Learning for Construction Safety Risk Assessment

Application of Ensemble Machine Learning for Construction Safety Risk Assessment

Modeling and analyzing multiteam coordination task safety risks in socio-technical systems based on FRAM and multiplex network: Application in the construction industry

The dynamically changing working environment and complex task requirements in modern industries emphasize the necessity of multiteam with different functions to work collaboratively. The multiteam coordination performance dominates the task process and the achievement of the superordinate goal, which will inevitably influence task safety. Although previous research has broadly identified the importance of inter-team cooperation to task performance, the analysis of the relationship between multiteam coordination structure, process, and system safety has not been fully addressed. Moreover, there is still a lack of an effective method to quantitatively analyze the functional interdependencies, interaction processes, and potential safety risks among teams in multiteam coordination tasks. Based on the construction industry background, this study combines FRAM and multiplex network to describe the multiteam coordination structure and task process variability. The relationship between multiteam coordination performance and construction safety risk is qualitatively and quantitatively analyzed. Case studies are adopted to verify the feasibility of the proposed method. The findings demonstrate the method's advantages in revealing the details of the influence of multiteam coordination performance on complex task safety, which is beneficial for developing targeted countermeasures to control safety risks from the perspective of the multiteam system.

Expanding Domain Knowledge Elements for Metro Construction Safety Risk Management Using a Co-Occurrence-Based Pathfinding Approach

Knowledge is a contribution factor leading to more effective and efficient construction safety management. Metro construction practitioners always find it difficult to determine what specialized knowledge is needed in order to lead to better safety risk management. Currently, domain knowledge elements are generally determined by experts, which is coarse-grained and uncomprehensive. Therefore, this paper aims to provide a structure of domain knowledge elements, using an automatic approach to expand domain knowledge elements (DKEs) from a big dataset of unstructured text documents. First, the co-word co-occurrence network (CCN) was used to find the connected knowledge elements, and then the association rule mining (ARM) was compiled to prune the weakly related subnetworks, leaving the strong associated elements. Finally, a list of DKEs in the metro construction safety risk management was obtained. The result shows that the obtained DKEs are more comprehensive and valuable compared to previous studies. The proposed approach provides an automatic way to expand DKEs from a small amount of known knowledge, minimizing the expert bias. This study also contributes to building a fine-grained knowledge structure for metro construction safety risk management. The structure can be used to guide safety training and help knowledge-based safety risk management.