Assaad, R H; Omran, A; Soliman, N and Assaf, G (2024) Prediction of the lateral pressure of self-consolidating concrete on construction formwork systems using machine-learning algorithms. Journal of Construction Engineering and Management, 150(9): 04024110, ISSN 0733-9364
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.
| Item Type: | Article |
|---|---|
| Index terms: | project stakeholder, artificial neural network, formwork, performance metric, face, testing, self-consolidating concrete, construction operation, laboratory, learning algorithm, body of knowledge, construction time, construction engineering, construction firm, forest, placement, construction demand, estimate, decision tree |
| Subjects: | building materials, construction operations, decision analysis, knowledge management, research management, building construction, algorithms, modelling and simulation, organization, project controls, performance measurement, environmental science, financial and cost management, engineering methods, sociology, professional practice, management, market analysis, psychology |
| Topics: | Research Practice, Digital Applications, Quality Management, Risk Management, Information Management, Engineering Principles, Sustainability, Cost Management, Time Control, Human Resources, Site Management, Business Strategy, Construction Technology, Stakeholder Management, Organizational Design |
| Descriptive scope: | 3 PCA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here