Performance-driven clause prioritization and smart contract synthesis for construction contract governance: A proof-of-concept framework and preliminary evaluation

Source abstract

Construction contracts govern payment, scheduling, change management, risk allocation, and dispute resolution, yet their scale and heterogeneity keep contract administration largely manual. Existing large language model (LLM) approaches extract and classify clauses but remain disconnected from project performance metrics, standard-form requirements, and executable contract logic. This paper proposes and preliminarily evaluates a retrieval-augmented contract intelligence framework that integrates clause extraction, semantic classification, KPI-aware importance ranking, standards alignment, discrepancy diagnostics, and constrained smart contract synthesis as a proof-of-concept pipeline. Clause importance is quantified by mapping contract language to four performance indicators (cost overrun impact, schedule delay impact, cash-flow adequacy, and dispute frequency); these indicators support a structured prioritization heuristic rather than an empirically validated predictive model. The framework was evaluated on 588 clauses from five executed contracts of a large public owner, spanning general contractor, construction manager, architect-engineer, commissioning, and design-build delivery. Moderate inter-annotator agreement (69.17%; Cohen's κ = 0.52) motivated a taxonomy revision from ten to eight categories, after which the domain-calibrated classifier achieved a macro-averaged F1 of 0.718 on a balanced validation set and 0.572 on the full corpus, whereas a chain-of-thought LLM prompt achieved 0.483 on the same balanced set. Clause rankings remained stable across alternative KPI weightings (Spearman ρ > 0.98). Preliminary findings indicate that contractual influence is concentrated in payment and approval provisions across delivery methods, and that governance risk in this corpus arises mainly through omission and weakening of provisions rather than direct contradiction of standard forms. Constrained template-based synthesis raised smart contract quality from 0.8/4.0 to 3.6/4.0 relative to unconstrained generation. These findings suggest that retrieval-augmented language models, combined with structured performance reasoning and domain-specific calibration, may support selective, performance-driven contract governance, and they motivate broader validation across owner types and contract families.