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

Featured Application The approach rapidly extracts reporting requirements from construction contracts and predicts overhead costs and durations associated with report preparation. Application of the approach is anticipated to provide the insights necessary to enhance contract negotiations, reporting workflow processes, and submittal procedures between clients and contractors. 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.

Automated summary

This study developed an automated framework to extract reporting requirements from construction contracts, predict overhead costs and durations associated with report preparation, and demonstrated its functionality and accuracy using real contracts with over 95% accuracy.