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contributor authorXuan Qi
contributor authorYongqiang Chen
contributor authorJingyi Lai
contributor authorFansheng Meng
date accessioned2024-04-27T22:23:48Z
date available2024-04-27T22:23:48Z
date issued2024/03/01
identifier other10.1061-JMENEA.MEENG-5604.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296560
description abstractIn the intricate domain of construction contracts, precise descriptions and measurements of contract structures are crucial. This study provides an objective analysis of the structure of construction contracts from a multifunctional perspective. A deep learning–based machine coding model was trained using 17 standard contracts and 35 actual contracts. The model was then used to code an additional 117 actual contracts. Statistical analysis was conducted to compare the distribution of the three functions (i.e., control, coordination, and adaptation) between standard and actual contracts. The results revealed that coordination has the highest contribution among the three functions. Moreover, actual contracts exhibit increased complexity compared with standard contracts, often containing additional control and coordination provisions related to project-specific obligations and tasks. The 117 actual contracts were further classified based on project delivery systems (PDSs) and pricing methods, and the impact of PDSs and pricing methods on the functional distribution was examined. The results showed more flexible adaptation and more complex control provisions specified in design-build/engineering, procurement, and construction (DB/EPC) and lump sum contracts. Theoretically, this study provides insights into objective measures in contract research and enriches the body of knowledge on the structure of construction contracts from a multifunctional perspective. Practically, professionals are provided with guidance on managing the complexity of each functional provision.
publisherASCE
titleMultifunctional Analysis of Construction Contracts Using a Machine Learning Approach
typeJournal Article
journal volume40
journal issue2
journal titleJournal of Management in Engineering
identifier doi10.1061/JMENEA.MEENG-5604
journal fristpage04024002-1
journal lastpage04024002-14
page14
treeJournal of Management in Engineering:;2024:;Volume ( 040 ):;issue: 002
contenttypeFulltext


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