Behavioural Forensics Of Contract Non-Compliance: A Neuro-Decision Framework For Predicting Disputes And Quantifying Intent In Construction And Energy Mega-Projects

Abstract

Contract non-compliance in construction and energy mega-projects represents a critical challenge, resulting in financial losses exceeding $50 billion annually in the global construction sector. This research introduces a novel Neuro-Decision Framework (NDF) that integrates behavioral forensics, cognitive neuroscience principles, and machine learning algorithms to predict contract breaches and quantify intentionality in stakeholder non-compliance. Through analysis of 127 construction and energy mega-projects across 15 years (2010-2025), we identified 42 behavioral indicators across three risk layers: environmental, decision-making process, and execution dynamics. The NDF demonstrated 94.7% accuracy in predicting disputes 6-12 months before contractual breach manifestation. Using neurobiological decision-making models and forensic behavioral analysis, we successfully classified non-compliance into four intent categories: (1) Inadvertent (no deliberate intent), (2) Conditional (context-dependent), (3) Strategic (calculated breach), and (4) Systemic (organizational dysfunction). The framework incorporates real-time cognitive biases, stakeholder communication patterns, and project contextual factors through ensemble machine learning techniques combining Random Forests, Gradient Boosting, and Neural Networks. Our findings reveal that intentionality quantification reduces dispute resolution time by 38% and improves settlement effectiveness by 42%. This neuro-decision framework offers a transformative approach to contract management, enabling proactive intervention strategies and evidence-based dispute resolution in high-stakes mega-projects.