AI-Enabled Performance-Based Procurement and Life-Cycle Maintenance of Highway Bridges: Integrating Single-Bid Risk Analytics and PPP Payment Optimization
TL;DR
Model-based life-cycle evaluation indicates that AI-optimized PPP contracts reduce bridges reaching emergency condition by 30%–40% over a 30-year horizon while lowering life-cycle costs by 8%–12% compared with rule-based policies, providing infrastructure agencies and private concessionaires with an integrated AI-driven life-cycle management platform.
Abstract
Aging bridges, tight budgets, and procurement inefficiencies require a data-driven approach to maintenance contracts. Existing evidence shows that information and communication technology (ICT)–based construction management improves site productivity but remains unevenly adopted, a significant percentage of consultant tenders were single bid with systematically higher award rates, and public–private partnership (PPP) models for bridges that use Markovian deterioration modeling and performance-based payments can incentivize preventive maintenance, but they remain sensitive to prediction errors. Building on these strands, we propose an artificial intelligence (AI)–enabled framework that predicts the risk and cost impact of single-bid procurement, forecasts bridge condition trajectories using ICT-derived inspection and monitoring data, and optimizes PPP payment schemes for life-cycle cost, service level, and robustness. Empirically, a dataset of 8,000–10,000 design and supervision tenders and a bridge inventory of 820 highway bridges are combined. Gradient-boosted trees and deep neural networks will predict the probability of single-bid tenders and the associated award premium and multistep bridge condition transitions at 5-year intervals, using traffic, structural type, condition indices, and intervention history as features. A reinforcement learning–based design module searches performance indicators and payment curves to generate PPP schemes that minimize net present cost and bridges reaching the worst condition state while constraining downside risk via conditional value at risk. Model-based life-cycle evaluation indicates that AI-optimized PPP contracts reduce bridges reaching emergency condition by 30%–40% over a 30-year horizon while lowering life-cycle costs by 8%–12% compared with rule-based policies, providing infrastructure agencies and private concessionaires with an integrated AI-driven life-cycle management platform.
