Applying data analytics for enhanced construction project performance through structural concrete rework predictive models.

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Stellenbosch : Stellenbosch University
ENGLISH ABSTRACT: Today’s world is driven by data-based decision-making that needs to be accurate to effectively solve engineering problems involving the prediction of failure, defects, and errors. Motivated by the fourth industrial revolution (Industry 4.0) that has enhanced the performance of construction industries on a global scale, this study discusses the development of a predictive machine learning model that can be used during the construction phase to manage structural concrete rework during site inspections. This model seeks to reduce uncertainties and minimise structural concrete rework during construction to enhance project performance. To develop the model, the research approach included an exploratory case study together with interviews with experienced professionals in structural concrete construction at the Hwange Expansion Project, a mega thermal power plant construction project in Hwange, Zimbabwe. The exploratory case study and expert interviews were conducted to establish a better understanding of the risk triggers that influence structural concrete rework in a typical construction project. A fictitious modelling dataset was then generated based on the results of a questionnaire survey conducted on structural concrete experts due to the lack of sufficient project data. Various data mining techniques were also employed to develop the prediction model following some steps of the Cross Industry Standard Practise for Data Mining (CRISP-DM) framework. This fictitious dataset was modelled on five classification algorithms whose performance was evaluated using the 20-fold cross-validation test. The Neural Network classifier recorded the highest performance with accuracy and precision of over 95%. To validate the performance of the Neural Network prediction model, the confusion matrix validation test was carried out on six datasets of varying size ranging from 500 to 10 000 data points. The results from the confusion matrix validation test indicated, as expected, that the larger the dataset, the more accurate and robust the model becomes in predicting new data outcomes. Based on these findings, it was established that data analytics in the form of predictive modelling can be used by organisations to reduce uncertainties and promote data-driven decision-making during structural concrete quality checks on site. It is recommended that construction industries employ data analytics as a project management tool not only to enhance the performance of construction projects but to build reference databases for further development of big data in the industry.
AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar
Thesis (MEng)--Stellenbosch University, 2021.
Structural concrete, UCTD, Engineering -- Machine learning, Construction projects, Failure analysis (Engineering), Survival analysis (Biometry), Analytics, Predictive