Ali, G M; Bouferguene, A and Al-Hussein, M (2023) Crane mat layout optimization based on agent-based greedy and reinforcement-learning approach. Journal of Construction Engineering and Management, 149(8): 04023067, ISSN 0733-9364
Abstract
The growing prevalence of modular construction, while it offers benefits in terms of productivity and sustainability, has led to increased use of heavy mobile cranes and related resources on construction sites. One significant resource associated with crane use is the crane mat, which offers practical mobile crane ground support against poor soil-bearing capacity. Due to increased use of crane mats, crane mat layout plans/drawings have become increasingly significant in today's construction industry. The present work describes an automated crane mat optimization framework for preparing crane mat layout plans/drawings built on an agent-based greedy algorithm and reinforcement learning. The proposed framework employs these approaches to achieve the maximum area with the minimum number of crane mats. The proposed framework is found to decrease the time required for preparing a crane mat layout plan/drawing (approximately 97% time saving) with more uniform and efficient crane mat planning outcomes (approximately 63% crane mat material reduction).
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | agent-based optimization; crane mat layout plans/drawings; greedy approach; mobile crane mats; reinforcement learning |
| Index terms: | agent, modular construction, drawing, construction site, construction industry, reinforcement, time saving, productivity |
| Subjects: | practitioner, work location, building materials, technical documentation, management, time analysis, industry analysis, building construction |
| Topics: | Design Practice, Time Control, Site Management, Business Strategy, Research Practice, Construction Materials, Roles and Professions, Construction Technology |
| Descriptive scope: | 3 PCT |
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