Jeong, E; Jang, J; Kim, T W and Lee, S (2025) Divide-and-conquer-based stratified models for predicting precast concrete installation times. Journal of Construction Engineering and Management, 151(9): 05025011, ISSN 0733-9364
Abstract
Precast concrete (PC) is increasingly recognized for offering sustainable building solutions in urban development. However, the realization of its benefits crucially depends on strategic planning and coordination throughout the entire process, from production to installation. This study is part of efforts to employ data-driven approaches to predict PC installation times. Specifically, this study employs a divide-and-conquer approach to stratify the PC installation process into two levels of detail, develops a distinct model for each level, evaluates these models under four regression algorithms, and determines the level of detail and the algorithms that are most suitable for creating reliable predictive models. Utilizing data collected from a logistics center in South Korea, this study found that the random forest model significantly outperformed others in forecasting the PC installation process. Additionally, the nine-stage decomposed model outperformed the two-stage aggregate model, particularly through enhanced feature selection tailored to the complexities of each work step. The study also identified significant variability in prediction accuracy among different work steps, with installation-related work steps showing more reliable predictions than unloading-related work steps. These refined predictions can support PC project managers by providing insights that identify time-intensive work steps and optimize planning to minimize delays, thereby enhancing overall project efficiency.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | divide-and-conquer; installation time; machine learning; precast concrete; predictive model |
| Index terms: | aggregate, South Korea, forecasting, project manager, efficiency, coordination, variability, precast concrete, urban development, strategic planning, accuracy, level of detail, forest, complexity, machine learning, sustainable building |
| Subjects: | materials science, environmental science, Geography, sustainable construction, urban design, profession, artificial intelligence, prediction and forecasting, technical documentation, building materials, professional development, management, performance management, systems engineering, statistical analysis |
| Topics: | Roles and Professions, Research Practice, Construction Materials, Information Management, Business Strategy, Organizational Design, Design Practice, Digital Applications, Urban Studies, Sustainability, Geographical Context, Engineering Principles, Quality Management |
| 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