Abstract
In recent years, significant attention has been paid to the use of Artificial Neural Network (ANN) techniques in the construction sector due to their ability to handle big data, predict risks, results accuracy, make reliable assessments, and solve complex nonlinear problems. This paper introduces a comprehensive sustainable methodology for risk assessment that is supported by artificial intelligence (AI). The methodology consists of four phases; phases one and two determine the risk factors impacting sustainable construction (SC) by collecting data from projects in Iraq (phase one) and conducting semi-structured interviews (phase two). Phase three applies a Failure Mode and Effect Analysis method to identify the risk factor values and apply an ANN model. Phase 4 uses the Absolute Percentage Deviation method to verify the proposed model. The study findings indicate that the ANN-based model provides more accurate and reliable risk assessments for SC projects than traditional methods. The model effectively integrates and analyses multiple variables and factors across the project life cycle, providing a comprehensive and dynamic view of risks and their potential impact.