Rashad, A; Hegazy, H; Zhang, J; Mahdi, I; Abdel-Rasheed, I and Ebid, A (2025) Developing preliminary cost estimates for foundation systems of high-rise buildings. International Journal of Construction Management, 25(6), pp. 682-698. ISSN 1562-3599
Abstract
This study provided a unique Artificial intelligence (AI) model for the cost estimation of the foundation system for high-rise buildings. First, parametric data research was conducted, including 180 examples of foundation systems for high-rise building models related to criteria such as average column spacing, floor height, slap load, overall weight, base shear, and over. In addition, moment, gravity load, seismic load, design load, various columns (mid-edge-corner), soil profile, bearing capacity, GWT, excavation depth, and foundation systems are all factors to consider (shallow and deep footing). The research encompassed column spacing (5.0, 7.0, and 9.0 m) and the number of stories from 15 to 35, with an average cost decrease of 15%. The AI model utilizes extensive machine learning algorithms to analyze structural and geotechnical parameters. It provides a predictive tool that improves decision-making in the preliminary design. The methodology includes collecting data, training models utilizing innovative neural network techniques and validating them against current cost estimation models. The findings indicate that the AI model significantly improves the accuracy of cost estimations, with a precision rate of 95%, compared to traditional methods. Moreover, the model presents a potential cost savings of up to 15%. Construction designers and decision-makers may utilize the study's findings to increase the accuracy of foundation system selection, lower costs and construction length, and improve overall project performance. Future work may involve applying this concept to infrastructure and substructure projects and collecting more data for benchmarking and empirical study.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; artificial neural networks; decision making; foundations; structural optimization; structural systems; substructure projects |
| Index terms: | cost estimate, cost saving, foundations, seismic load, artificial intelligence, empirical study, project performance, designer, machine learning, high-rise building, training model, benchmarking, preliminary design, methodology, decision-making, excavation, neural network, column, accuracy, artificial neural network, cost estimating |
| Subjects: | project management theory and practice, professional development, economics, performance measurement, design practice, decision analysis, profession, financial and cost management, artificial intelligence, modelling and simulation, research methods, structural engineering, construction type, construction operations, curriculum development |
| Topics: | Risk Management, Project Management, Engineering Principles, Education, Quality Management, Construction Technology, Roles and Professions, Research Practice, Information Management, Cost Management, Site Management, Design Practice, Digital Applications |
| Descriptive scope: | 4 PCTA |
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