Review on construction risk and development of risk management procedural index – A case study from Chennai construction sector

Kumar, Kaushal; Narayanan, Rm.

doi:10.1016/j.matpr.2020.08.606

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…After collecting and filtering the required accident data, it is necessary to further extract attributes and factors to describe the accidents. There are plenty of literature to provide explanations for risk identification methods of construction accidents (Sharan and Narayanan, 2020;Siraj and Fayek, 2019). Attribute classification initially referred to the research results of Tian et al (2021).…”

Section: Methodsmentioning

confidence: 99%

An association rule mining model for evaluating the potential correlation of construction cross operation risk

Chen

Tian

Liu

et al. 2022

ECAM

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PurposeCross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.Design/methodology/approachThe concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.FindingsThe application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.Originality/valueThis is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

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Section: Methodsmentioning

confidence: 99%

An association rule mining model for evaluating the potential correlation of construction cross operation risk

Chen

Tian

Liu

et al. 2022

ECAM

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“…Construction risk usually involves many factors, such as construction workers risk behavior [23], 4M1E [24], and management risk [25]. In terms of construction risk identification [26], analysis [27], evaluation [28], and risk control [29], scholars have carried out abundant research and found that construction risk factors have different dimensions, such as the dimension of participants, the dimension of construction control objectives, the dimension of engineering nature, the dimension of total factors, and the dimension of project management.…”

Section: Status Of Construction Riskmentioning

confidence: 99%

Risk Assessment of Urban Rail Transit PPP Project Construction Based on Bayesian Network

Liu

Jiao

et al. 2021

Sustainability

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In recent years, the accident rate of urban rail transit PPP (public–private partnerships) project under construction has been relatively high, and the issue of security risks has attracted great attention from all walks of life. Therefore, it is necessary to identify, analyze, and evaluate the issue of security risks of the urban rail transit PPP project. This paper takes the PPP project of urban rail transit as the object. Through offline interviews and surveys and online questionnaires, this paper focuses on identifying and analyzing the risks brought by the introduction of PPP mode to the urban rail transit project and its action mechanism. The risk evaluation method based on Bayesian network model is studied, which is described from three dimensions: risk occurrence probability, risk reasoning, and risk sensitivity. Finally, an example of Xuzhou Metro Line 3 is given to verify the feasibility of the proposed method. This study provides a reference basis for relevant practitioners and promotes the healthy development of the industry.

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“…With the rapid development of the construction industry, new types of buildings, such as supertall, green, and intelligent buildings, are driving the expansion of the construction scale and the diversification of design structures, thereby increasing both the volume of work and construction complexity, while imposing greater demands on safety management [2][3][4]. However, factors unique to the construction industry-such as inconsistent labor quality, high resource demands, long project cycles, and complex construction conditions-position it as one of the industries with the highest safety risks [5][6][7]. The construction safety production sector currently confronts a series of deep-rooted challenges and issues involving various facets of safety production risk management [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…All the samples x i classified to the jth leaf node can be formed into a sample set using the mathematical expression I j = {i|q(x i ) = j}. For a fixed structure q(x), it can be obtained through quadratic derivation that the optimal weight w * j of the leaf node j is (6).…”

mentioning

confidence: 99%

Research on the Prediction of Sustainable Safety Production in Building Construction Based on Text Data

Fan,

Wang,

Liu

et al. 2024

Sustainability

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Given the complexity and variability of modern construction projects, safety risk management has become increasingly challenging, while traditional methods exhibit deficiencies in handling complex dynamic environments, particularly those involving unstructured text data. Consequently, this study proposes a text data-based risk prediction method for building construction safety. Initially, heuristic Chinese automatic word segmentation, which incorporates mutual information, information entropy statistics, and the TF-IDF algorithm, preprocesses text data to extract risk factor keywords and construct accident attribute variables. At the same time, the Spearman correlation coefficient is utilized to eliminate the multicollinearity between feature variables. Next, the XGBoost algorithm is employed to develop a model for predicting the risks associated with safe production. Its performance is optimized through three experimental scenarios. The results indicate that the model achieves satisfactory overall performance after hyperparameter tuning, with the prediction accuracy and F1 score reaching approximately 86%. Finally, the SHAP model interpretation technique identifies critical factors influencing the safety production risk in building construction, highlighting project managers’ attention to safety, government regulation, safety design, and emergency response as critical determinants of accident severity. The main objective of this study is to minimize human intervention in risk assessment and to construct a text data-based risk prediction model for building construction safety production using the rich empirical knowledge embedded in unstructured accident text, with the aim of reducing safety production accidents and promoting the sustainable development of construction safety in the industry. This model not only enables a paradigm shift toward intelligent risk control in safety production but also provides theoretical and practical insights into decision-making and technical support in safety production.

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Review on construction risk and development of risk management procedural index – A case study from Chennai construction sector

Cited by 8 publications

References 21 publications

An association rule mining model for evaluating the potential correlation of construction cross operation risk

An association rule mining model for evaluating the potential correlation of construction cross operation risk

Risk Assessment of Urban Rail Transit PPP Project Construction Based on Bayesian Network

Research on the Prediction of Sustainable Safety Production in Building Construction Based on Text Data

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