Machine Learning–Based Decision Support Framework for Construction Injury Severity Prediction and Risk Mitigation

Gondia, Ahmed; Ezzeldin, Mohamed; El‐Dakhakhni, Wael

doi:10.1061/ajrua6.0001239

Cited by 11 publications

(6 citation statements)

References 71 publications

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“…The study indicated that risk-mitigation had positive effect on project quality. Gondia et al (2022) study on machine learning based decision support framework for construction injury severity prediction and risk mitigation, showed that risk-mitigation had positive and significant effect on project quality.…”

Section: Empirical Reviewmentioning

confidence: 99%

The Dimensional Effect of Optimal Project Risk Management Practices on Project Quality of Selected Construction Companies in Nigeria

Herbertson,

Makinde,

Akinlabi

et al. 2023

IJIRETSS

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In the construction industry, ensuring high-quality standards is a critical aspect of construction projects and plays a pivotal role in the sustained viability and triumph of contemporary construction companies. However, existing literature highlights the persistently subpar levels of project quality within the construction sector. This situation is manifested through cost overruns, time delays, deficient project definition, and fluctuations in project scope, which are indicative of insufficient project risk management practices. Although several studies have examined project risk management practices, little emphasis has been placed on evaluating their impact on quality in the construction industry. Hence, this study examined the dimensional effect of optimal project risk management practices on project quality of selected construction companies in Nigeria. Survey research design was adopted. The population was 202 top management and mechanical department staff of three selected construction companies in Lagos State, Rivers State, and the Federal Capital Territory of Abuja, Nigeria. A sample size of 176 was found to be usable. A validated questionnaire was adopted for data collection. Cronbach’s alpha reliability coefficients for the constructs ranged from 0.74 to 0.98. The response rate was 87.1%. Data were analyzed using the Smart partial least squares structural equation modeling (PLS-SEM) software, which allowed for the testing of path analysis and hypotheses. A confirmatory factor analysis (CFA) was employed to assess the factor loading of the variables. Findings indicated that project risk management practices had significant effect on project quality of selected construction companies in Nigeria (Adj R2 = 0.54, F2 = 0.058, Q2 = 0.507, p < 0.05). The study concluded that project risk management practices improved the quality of the selected construction companies in Nigeria. The study recommended that project managers should establish and implement effective quality management systems that outline clear processes and procedures for ensuring project quality. Develop quality control plans that include inspection, testing, and monitoring activities throughout the project lifecycle. Incorporate quality management standards and best practices, such as ISO 9001, to enhance quality assurance processes.

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Section: Empirical Reviewmentioning

confidence: 99%

The Dimensional Effect of Optimal Project Risk Management Practices on Project Quality of Selected Construction Companies in Nigeria

Herbertson,

Makinde,

Akinlabi

et al. 2023

IJIRETSS

View full text Add to dashboard Cite

show abstract

“…Project success factors are components that must go well to guarantee the manager and organization's success [13]. In a study by Nguyen et al, five key success factors were extracted from 20 factors, which included competent project manager, providing sufficient financial resources until the end of the project, competent and multi-disciplinary project team, commitment to the project, and access to resources [28][29][30]. Evidently, there is a connection between project quality and project performance.…”

Section: Project Success Criteria and Factorsmentioning

confidence: 99%

“…Afterward, the complexity and interdependence of the system were discovered through the analysis of exploratory data. Ultimately, the work presented here benefited from machine learning's power to facilitate evidence-based decision-making [30]. A machine learning approach can be presented to predict the project performance based on various criteria of entrepreneurial orientation and entrepreneurial attitude of individuals to analyze and predict project success [31].…”

Section: Using Machine Learning In Project Success Martínez and Ferná...mentioning

confidence: 99%

Analysis of Factors Affecting the Success of Sustainable Development Projects with the Help of Machine Learning Tools

Zhijun

Hsieh

Davoudi

2022

Discrete Dynamics in Nature and Society

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Sustainable development projects are a group of development projects created with the aim of sustainable urban growth and development. To achieve development, it is essential to pay attention to the existence of projects. The point to consider is the threat of these expensive assets by all kinds of risks, such as floods, earthquakes, wars, mistakes, and price fluctuations, during the life cycle of projects from the beginning of their idea to the end of their useful life. Hence, the main objective of the study is to analyze the criteria and factors of project success and different machine learning strategies to achieve success and predict specific construction performance. To meet that aim, the research employs the descriptive approach, and analytical and logical aspects are derived from various sources such as research articles, published materials, online websites, books, and articles. The study’s results reveal that employing machine learning tools and algorithms to create a link between project success factors and criteria and prediction can bring multiple advantages, including high accuracy, ease of use, and inference for decision-making. It can be concluded that algorithmic solutions could be integrated in a manner that project managers can adequately utilize to enhance project success by eliminating potential risks and guiding the project toward attaining its objectives.

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“…This will enable practitioners to both improve the necessary and relevant OHS training and control the conditions/environment within the framework of OHS protocols. In this context, construction safety management literature draws attention to artificial intelligence (AI), or more specifically machine learning (ML) applications to predict accident outcomes and correspondingly apply necessary prevention or mitigation measures [10,[12][13][14][15]. By using data-driven ML methods, various output variables such as whether an occupational accident will occur [16], whether it will result in fatality or injury [17], whether unsafe behaviour will be observed [18], or in which time period it will occur more frequently, can be predicted.…”

Section: Introductionmentioning

confidence: 99%

Determining the Short-term Susceptibility of Construction Workers to Occupational Accidents Using Stochastic Gradient Boosting

Koç¹

2023

JCEMI

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Employees working on construction projects have a higher risk of experiencing occupational accidents compared to workers in other sectors. Newly employed workers might face an occupational accident shortly after they start working due to not being able to notice risky environmental conditions. Despite existing studies in construction safety literature focusing on several output variables such as fatality, accident type or accident severity predictions, no studies have examined the short-term susceptibility of construction workers to occupational injuries. This study aims to develop a model to predict construction workers' susceptibility to short-term occupational accidents using interpretable machine learning (ML) methods. Hence, the primary research objective is to identify construction workers who have high probability of experiencing an occupational accident shortly after their employment. In this respect, a national dataset of occupational accidents encountered in the construction industry in Turkey was collected and subjected to various pre-processing elements (data cleaning, data scaling, and data resampling) to prepare the data for prediction. At the processing step, Stochastic Gradient Boosting (SGB) algorithm was applied for the classification purpose.In the next step, Shapley Additive Explanations (SHAP) was used as an interpretable artificial intelligence algorithm to explain how, to what extent, and in which direction the input variables affect the prediction scheme, which is another distinguishing feature of the present study compared to past studies in the subject matter. Results show that the proposed SGB model is a powerful detector for the classification problem and salary of workers, past accident in the company, and number of workers in the company were the most influencing factors. Overall, this study contributes to practice by improving the safety conditions of the newly employed workers as well as minimizing their accident probability through intensified safety training. Given that contemporary safety management applications demand a new set of data-driven inputs, proposed model is expected to help industry professionals and safety managers apply more robust safety risk mitigation and/or prevention measures.

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Machine Learning–Based Decision Support Framework for Construction Injury Severity Prediction and Risk Mitigation

Cited by 11 publications

References 71 publications

The Dimensional Effect of Optimal Project Risk Management Practices on Project Quality of Selected Construction Companies in Nigeria

The Dimensional Effect of Optimal Project Risk Management Practices on Project Quality of Selected Construction Companies in Nigeria

Analysis of Factors Affecting the Success of Sustainable Development Projects with the Help of Machine Learning Tools

Determining the Short-term Susceptibility of Construction Workers to Occupational Accidents Using Stochastic Gradient Boosting

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