Automated Extraction and Time-Cost Prediction of Contractual Reporting Requirements in Construction Using Natural Language Processing and Simulation

Applied Sciences

Malak Al Hattab¹  Parinaz Jafari¹  Emad Mohamed¹  Simaan AbouRizk¹ 

[1] 5-080 NREF, Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2W2, Canada;

关键词: construction reports;    construction contracts;    natural language processing;    machine learning;    simulation modeling;   

DOI  :  10.3390/app11136188

来源: DOAJ

【 摘 要 】

Due to a lack of suitable methods, extraction of reporting requirements from lengthy construction contracts is often completed manually. Because of this, the time and costs associated with completing reporting requirements are often informally approximated, resulting in underestimations. Without a clear understanding of requirements, contractors are prevented from implementing improvements to reporting workflows prior to project execution. This study developed an automated reporting requirement identification and time–cost prediction framework to overcome this challenge. Reporting requirements are extracted using Natural Language Processing (NLP) and Machine Learning (ML), and stochastic simulations are used to predict overhead costs and durations associated with report preparation. Functionality and validity of the framework were demonstrated using real contracts, and an accuracy of over 95% was observed. This framework provides a tool to rapidly and efficiently retrieve requirements and quantify the time and costs associated with reporting, in turn providing necessary insights to streamline reporting workflows.

【 授权许可】

Unknown