Risk assessment of deep excavation construction based on combined weighting and nonlinear FAHP

Liu, Shihao; Song, Zhanping; Zhang, Yong; Guo, Desai; Sun, Yunliang; Zhang, Tao; Xie, Jiangsheng

doi:10.3389/feart.2023.1204721

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…In fact, in the field of construction safety risk, the research results of the WBS-RBS model have increased significantly in recent years [15,16,[30][31][32][33][34][35][36], while the research results of the 4M1E model have not increased significantly.…”

Section: Discussion On Different Risk Identification Methodsmentioning

confidence: 99%

Safety Risk Analysis of Urban Viaduct Construction Based on Dynamic Weight

Ran,

Wang,

Fang

et al. 2024

Buildings

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The safety risk analysis of urban elevated bridge construction is an important management method to reduce the loss of safety accidents, and it has significant scientific research value and engineering application value. Therefore, this study proposes a novel analysis method to address these challenges. Firstly, this paper constructs a Work Breakdown Structure (WBS)–Risk Breakdown Structure (RBS) matrix for the safety risk of urban elevated bridge construction in order to achieve a comprehensive and complete identification of the indicator system. Then, a combination of static weights and dynamic weights calculation methods is developed. The static weights are obtained using the analytic hierarchy process, while the dynamic weights are obtained based on the relationship between the dynamic scores of construction safety risk indicators in different construction stages and the preset evaluation levels. Finally, a case study of the Longlingshan elevated bridge project in Wuhan, China, is conducted to validate the feasibility of the proposed model and its potential application in projects. The case analysis for the first time reveals that with the progress of construction, the weights of each indicator continuously change, and the secondary indicators related to environmental factors, such as extreme high-temperature weather, undergo the greatest changes. A comparison of different dynamic weight calculation methods is conducted to highlight the advancement of the proposed model. The research findings of this paper will provide new insights and guidance for improving the construction safety of urban elevated bridge projects.

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Section: Discussion On Different Risk Identification Methodsmentioning

confidence: 99%

Safety Risk Analysis of Urban Viaduct Construction Based on Dynamic Weight

Ran,

Wang,

Fang

et al. 2024

Buildings

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show abstract

“…Regarding the model rationale, the experts agreed that the logic for the establishment process is reasonable. Specifically, using the WBS-RBS analysis to identify the CSRFs and the OBS-WBS analysis to determine the construction workers' set are common project management tools [61,62]. For example, You et al [62] applied a WBS-OBS analysis to determine the CSRFs of the subway tunnel shield construction.…”

Section: Expert Interview For Model Validationmentioning

confidence: 99%

Research on Construction Workers’ Safety Risk Sharing in Tunneling Projects Based on a Two-Mode Network: A Case Study of the Shangwu Tunnel

Cai,

Huang,

Peng

2023

Buildings

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There is a high level of construction safety risk shared among construction workers in tunneling projects due to collaboration on the narrow and semi-enclosed construction site. However, no one has reported on this. Therefore, this paper proposes a new network model to explore risk-sharing features among construction workers based on a two-mode network. That model represents a new personnel safety management tool to provide suitable risk mitigation for tunneling projects. First, the work breakdown structure (WBS)–risk breakdown structure (RBS) method was employed to identify construction activities, risk resources, and construction safety risk factors (CSRFs). Subsequently, the two-mode WBS–RBS matrix was further established. The construction workers’ sets were determined based on the organization breakdown structure (OBS)–WBS method and a two-mode OBS–WBS matrix was established. By applying the construction activities in the WBS tree carrying the CSRFs as the link, a two-mode OBS–RBS network was established by converting the two-mode WBS–RBS and OBS–WBS matrices. Hence, taking CSRFs allocated by several construction workers as a basis for network generation, the construction workers’ risk-sharing network was further established. Centrality analysis identified the network characteristics and determined the most important construction workers in risk network. For example, this model was employed to explore the whole network characteristics of the Shangwu Tunnel and identify the workers in key positions in the risk-sharing network. Expert interviews demonstrated the model’s rationality and practicality. The results show that each construction worker’s safety risk-sharing degree in the Shangwu tunnel differed and reached varying levels. However, the staff from the engineering management department were in the key position of the risk-sharing network. Collectively, this model can help construction workers understand their risk-sharing degree to improve their safety awareness and adjust their attitude toward safety accordingly. Moreover, this strategy provides project managers with the necessary information to more effectively allocate safety resources and to be cognizant of the safety quality of each construction worker according to the different risk-sharing degrees.

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Prediction and analysis of time series data based on granular computing

Yin

2023

Front. Comput. Neurosci.

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The advent of the Big Data era and the rapid development of the Internet of Things have led to a dramatic increase in the amount of data from various time series. How to classify, correlation rule mining and prediction of these large-sample time series data has a crucial role. However, due to the characteristics of high dimensionality, large data volume and transmission lag of sensor data, large sample time series data are affected by multiple factors and have complex characteristics such as multi-scale, non-linearity and burstiness. Traditional time series prediction methods are no longer applicable to the study of large sample time series data. Granular computing has unique advantages in dealing with continuous and complex data, and can compensate for the limitations of traditional support vector machines in dealing with large sample data. Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed, and the basic principles of traditional time series forecasting methods and granular computing are investigated. Secondly, in terms of predicting the trend of data changes, it is proposed to apply the fuzzy granulation algorithm to first convert the sample data into coarser granules. Then, it is combined with a support vector machine to predict the range of change of continuous time series data over a period of time. The results of the simulation experiments show that the proposed model is able to make accurate predictions of the range of data changes in future time periods. Compared with other prediction models, the proposed model reduces the complexity of the samples and improves the prediction accuracy.

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Risk assessment of deep excavation construction based on combined weighting and nonlinear FAHP

Cited by 3 publications

References 30 publications

Safety Risk Analysis of Urban Viaduct Construction Based on Dynamic Weight

Safety Risk Analysis of Urban Viaduct Construction Based on Dynamic Weight

Research on Construction Workers’ Safety Risk Sharing in Tunneling Projects Based on a Two-Mode Network: A Case Study of the Shangwu Tunnel

Prediction and analysis of time series data based on granular computing

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