## 9 Conclusions  
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. To overcome the shortcomings of the traditional manual approach, this study developed a framework for reporting requirement extraction based on NLP--a domain-specific and application-oriented text classification process--that is capable of automatically identifying reporting requirements from contractual documents to considerably reduce the time and effort required to extract reporting requirements. To account for project uncertainties due to variation or unforeseeable events that may occur during execution, a Monte Carlo simulation was used to predict the time and cost needed to complete reporting requirements. The model begins by collecting textual data, in this case the sentences and terms in contractual documents, which describe the reporting requirements mandated by the client. Rule-based and ML-based classification methods were developed, and their performances were evaluated. The performance of rule-based classification using different sets of n-grams was assessed, with an accuracy of 99.27% achieved using bi-grams as rules. Application of lemmatization to and removal of stop-words from the bi-gram rules resulted in a recall and F1-score of 88% and 92% for the \"true\" category, respectively. Four ML algorithms were also implemented, and their performance was assessed under different pre-processing settings and feature engineering techniques. All of the ML classification models achieved promising accuracies of over 95%; notably, XGBOOST achieved the highest recall value of 89% after parameter tuning. Then, numerical data regarding report preparation times and associated resources (based on prior experience of experts) were provided by an industrial partner and were used to predict the time and cost required to complete the reporting requirements detailed in the contractual documents. Input of these data into the Monte Carlo simulation model resulted in a mean cumulative reporting duration and cost of 5083 h and 304,939 CAD, respectively. During the bidding and contract negotiation phase of a project, decision makers can now use the proposed framework to automatically review reporting requirements prior to accepting the contractual agreement. Not feasible using time-consuming, traditional extraction methods, the extraction speed of the framework allows decision makers to identify and subsequently negotiate difficult and/or inefficient reporting requirements prior to signing. If contract conditions are unfavorable to the contractor in terms of project reporting cost, a revision of contract conditions may be requested or a contract may be abandoned by contractors to prevent further loss. With a thorough and realistic understanding of contract reporting requirements, contractors can focus on establishing the best means, methods, pricing, and schedules for completing the proposed project. Conceptualization, P.J. and S.A.; data curation, P.J., M.A.H. and E.M.; formal analysis, P.J. ; funding acquisition, S.A.; investigation, P.J. ; methodology, P.J. ; project administration, S.A.; software, P.J. ; supervision, S.A.; validation, P.J. ; visualization, P.J. and M.A.H. ; writing--original draft, P.J. ; writing--review and editing, M.A.H., E.M. and S.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Sciences and Engineering Research Council of Canada through a Collaborative Research and Development Grant (CRDPJ 492657). Not Applicable. All data used in the study were provided by a third party. Direct requests for these materials may be made to the provider indicated in the Acknowledgments. Modelsand code that support the findings of this study are available from the corresponding author upon request and with permission from the partner indicated in the Acknowledgments.