Automating Pre-award Contractual Risks Identification Process using Artificial Neural Networks

Document Type : Original Article

Authors

1 GP 124 Bld 15

2 Professor of Construction Projects Management, Structural Engineering Department, Ain Shams University (ASU), Cairo, Egypt

3 Professor of Construction Project Management Structural Engineering Department, Ain Shams University (ASU), Cairo, Egypt.

4 Assistant Professor of Construction Projects Management Structural Engineering Department, Ain Shams University (ASU) and The American University in Cairo (AUC), Cairo, Egypt

Abstract

Overview project management across various domains focusing on the automated identification of contractual risks during the pre-award stage using Artificial Neural Networks (ANNs). The pre-award phase of construction projects involves evaluating contractual risks to ensure successful project execution. Traditional methods of contract analysis are often time-consuming, manual, and prone to human error. To address these challenges, the integration of AI, specifically ANNs, offers a promising solution to automate risk identification and assessment, leading to more efficient decision-making processes. This research explores methodologies for developing a sophisticated and reliable model, with the input of software developers serving as subject-matter experts.

This research conducts a comprehensive literature review on AI applications in construction project management and risk assessment. The expected outcome of this research is to present a suitable way to development of an ANN-based framework for automating risk identification regarding contractual risks during the pre-award stage, showing the benefits of automating this stage in the project life cycle.

The objective of this research is to harness comprehensive data, including prior studies on risk identification in contract stipulations and the application of Artificial Neural Networks in construction contracts. The study's scope is limited to construction projects governed by the FIDIC 99 Redbook, showing previously limited trails in the same regards, after analyzing them, introducing a suitable direction towards automating this process. The findings of this research will direct future research to the development of a comprehensive contract risk analysis model utilizing ANN, enhancing decision-making processes in construction projects.

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