Risks assessment in the construction of infrastructure projects using artificial neural networks

Ibrahim, M.A.; Mohamed, Ahmed Gouda

doi:10.1080/15623599.2022.2156902

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…The remaining of those recent studies was related to construction projects in general. Nabawy and Gouda Mohamed [16] classified infrastructure project risk factors by their risk scores obtained by the probability of occurrence and impact on cost using a sequence of methods. The study used a risk breakdown structure to classify the risks from the literature, a checklist, a questionnaire, and Back Propagation Multi-Layer Perceptron (BP-MLP) Artificial Neural Network to generate the final classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative Risk Assessment Model and Optimization in Infrastructure Fast-Track Construction Projects

2023

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The construction industry has extensively applied the fast-tracking approach to the demanding need for the fast delivery of infrastructure projects. However, the fast-track strategy might be threatened by distinctive risks or changes in risk characteristics that emerge when activities are overlapped (overlapping risks). This article proposes a risk assessment simulation model to quantify the economic impact of overlapping risks on fast-track infrastructure projects. The model uses Monte Carlo simulation and a proprietary engine solution for the optimization procedure. It quantifies the overlapping risk impacts in the project duration and cost that could originate in three different overlapping degrees and evaluates the optimal overlapping degree to reduce the impact of the overlapping risks. The model demonstration used a commercial renovation project. The results suggest that overlapping risks have a high potential impact on the total cost, although with a high probability of attaining the target duration. Eight top risks affected the total duration, cost, or both. The optimum overlapping to reduce the economic impact and achieve the target project duration combines different overlapping degrees. This study contribution is a model for fast-track projects considering overlapping risks, their impact characteristic as a distribution, and the potential relationship between these risks.

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Section: Literature Reviewmentioning

confidence: 99%

Quantitative Risk Assessment Model and Optimization in Infrastructure Fast-Track Construction Projects

2023

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“…The hidden layer is many processing elements between the output layer and the input layer, which are interrelated and layered [129,130]. The network learns from example data by adjusting the strength or weight of the connections between neurons, enabling it to make predictions or classify based on new inputs [131]. In dealing with risk assessment, the sources of information are neither complete nor illusory.…”

Section: Wuli-shili-renli (Wsr) Systemmentioning

confidence: 99%

“…rons, enabling it to make predictions or classify based on new inputs [131]. In dealing with risk assessment, the sources of information are neither complete nor illusory.…”

Section: Wuli-shili-renli (Wsr) Systemmentioning

confidence: 99%

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Junjia,

Alias,

Haron

et al. 2023

Buildings

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Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on “ergonomics”, “machine learning”, “computer simulation”, and “wearable sensors”. Secondly, the previous 80 factors contributing to hoisting risks were summarized from a “human–equipment–management–material–environment” perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT’s interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research.

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Reinvigorating the process of contractor selection in adaptive reuse projects: a novel decision-making and evaluation paradigm

Gouda Mohamed,

Hathout

2024

International Journal of Construction Management

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Risks assessment in the construction of infrastructure projects using artificial neural networks

Cited by 5 publications

References 33 publications

Quantitative Risk Assessment Model and Optimization in Infrastructure Fast-Track Construction Projects

Quantitative Risk Assessment Model and Optimization in Infrastructure Fast-Track Construction Projects

A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction

Reinvigorating the process of contractor selection in adaptive reuse projects: a novel decision-making and evaluation paradigm

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