| description abstract | Construction firms face considerable challenges in relation to finding cost-effective formwork solutions to meet increased construction demands. Project stakeholders have relied on self-consolidating concrete (SCC) to speed up the construction time because SCC is highly fluid and has numerous advantages compared to traditional concrete. To withstand SCC’s high fluidity, formwork systems should be robust. Although previous research has experimentally examined various characteristics of SCC, few research studies have used machine-learning algorithms to estimate or predict the lateral pressure exerted by SCC on formwork systems. Hence, this study addressed this knowledge gap by proposing a machine-learning approach to predict the lateral pressure of SCC on vertical formwork systems. First, laboratory tests were performed to collect data on lateral pressure measurements, material factors, placement conditions, and formwork characteristics affecting the SCC lateral pressure on formwork systems. Second, four supervised machine-learning algorithms were considered in this study: k-nearest neighbor (KNN), artificial neural network (ANN), decision tree (DT), and random forest (RF). Third, the hyperparameters of the machine-learning algorithms were tuned, and their performance metrics were compared. Fourth, the most accurate predictive machine-learning model was verified on an unseen testing set. The results showed that the RF machine-learning algorithm was the best model for predicting the lateral pressure of SCC on formwork systems, with a mean percentage error of 0.8%, a mean absolute percentage error of 4.29%, and a coefficient of determination R2 of 0.9548. This study adds to the construction engineering and management body of knowledge by developing a machine-learning predictive model that can be used to accurately assess the lateral pressure exerted by SCC on formwork, which helps to ensure safe design of formwork systems and economic construction operations in formwork-related activities. | |
