Abstract

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.

Highlights

  • Work within the construction industry is allocated through construction contracts [1], which include information such as instructions, definitions, supporting statements, and contractual requirements that detail the standards and project specifications of the client [2].A core component of construction contracts is reporting and information requirements, which require contractors to periodically submit various reports detailing different aspects of project progress to the client [3]

  • This study has developed a framework capable of (1) automating the identification and extraction of reporting requirements and (2) predicting and analyzing the overhead costs and durations associated with report preparation

  • Automating the reporting requirement extraction process and estimating its associated time–cost implications are expected to reduce the effort, time, and overhead costs expended by the multiple personnel involved

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Summary

Introduction

Work within the construction industry is allocated through construction contracts [1], which include information such as instructions, definitions, supporting statements, and contractual requirements that detail the standards and project specifications of the client [2]. Real contractual documents from an actual case study were used to (1) develop and refine the reporting requirement extraction module and (2) demonstrate the functionality and validity of the complete framework This framework provides practitioners and researchers with an automated tool to more efficiently identify reporting requirements and quantify the time and costs associated with report preparation. Practical application of this approach is anticipated to provide decision makers with the insights necessary to enhance contract negotiations, reporting workflow processes, and submittal procedures between clients and contractors, in turn increasing value for all project stakeholders

Construction Reporting
NLP Applications in Construction
Research Gap
Framework Overview
Extraction Module
Impact
Machine Learning-Based Classification
10. As“true”
Comparison of Classification
Prediction Module
Case Study
Data Collection
Validationof ofExtraction
Validation of Prediction Module
Discussion
Findings
Limitations and Future
Conclusions
Full Text
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