## 8 Limitations and Future Work  
An automated approach for rapidly extracting reporting requirements from contractual documents and predicting the time and cost required to complete reporting activities was developed. Although the functionality of the proposed framework was demonstrated using real contractual documents from an actual case study, the following points should be considered.  
First, the extraction module was developed using a labeled dataset obtained from one set of contract and specification documents for an oil and gas project. While the extraction module is expected to be applicable--in its current form--to all construction contracts with similar characteristics (e.g., terminology, document structure, and/or report structure), the development methodology described may need to be reapplied for other contract types. Moreover, the classification models were trained using a limited amount of training data. The comparatively low performance of the classification models for the \"true\" class may be due to the size of the \"true\" dataset (i.e., an imbalanced data problem). Future work should examine the impact of increasing the training dataset through the incorporation of additional contract documents to enhance the performance of the classification models. With sufficient training data, the extraction module is anticipated to achieve the desired performance of 100% recall for the \"true\" class (i.e., identification of all reporting requirements).  
Second, the success of the prediction module is highly dependent on accurately modeling the inputs. One of the difficulties in analyzing probabilistic processes inherent to construction is defining the probability distributions that best reflect the uncertainties associated with each variable. The more accurate the model of the inputs, the more closely the simulation model mimics real-life behavior. A primary constraint for any simulation model, therefore, is the time and effort required to collect pertinent and correct information, as well as processing it for input into the model. While the resources and time required from construction sites and administration offices to complete reporting requirements are not commonly recorded, efforts to improve data collection related to project reporting process are expected to improve model results.  
Third, contract documentation remains an immature area of practice, and more reliable and efficient approaches to better and more rapidly understand contract requirements are needed. Future work should focus on providing a holistic solution to this problem, such as writing contracts using a structured-database approach. While this would provide seamless integration between clients and contractors (thereby alleviating the need for rule-based/ML-based model (re)training), achieving this ideal will require a tremendous amount of input, effort, and collaboration among all of the stakeholders involved in a project. Additionally, methods for dealing with modifications or alternate arrangementswill need to be researched and developed. Consequently, the framework proposed here provides a much-needed interim solution as these more holistic solutions are pursued.