人工神经网络
计算机科学
人工智能
系统工程
机器学习
工程类
作者
Behzad Abbasnejad,Araz Nasirian,Sophia Xiaoxia Duan,Abebe Diro,Madhav Prasad Nepal,Yiliao Song
出处
期刊:Journal of the Construction Division and Management
[American Society of Civil Engineers]
日期:2024-03-09
卷期号:150 (5)
被引量:3
标识
DOI:10.1061/jcemd4.coeng-14262
摘要
Recognizing the current level of BIM implementation in firms can be challenging without a quantitative method.However, there is no quantitative method to assess firms' current level of BIM implementation.This study aims to address this gap.The study begins with a literature review to identify 27 BIM implementation enablers, followed by interviews with three firms to score their performance in each enabler.A mathematical model is then developed to score a firm's BIM implementation.One million random scenarios are simulated for each firm's enablers' score.The simulation results are fed into a designed feature pairing neural network, which provides a customized best course of action for each firm.The study contributes to the body of knowledge by proposing a novel quantitative approach for measuring the current level of BIM implementation and providing data driven advise for steering the BIM implementation process.
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