Uncertainty in infrastructure deterioration and maintenance cost forecasting continues to weaken the effectiveness of conventional maintenance contracts, contributing to budget overruns, delayed interventions, and inefficient risk allocation. This study develops a machine learning (ML)–enhanced adaptive performance-based maintenance contract (APBMC) framework that integrates hybrid Markov–ML deterioration forecasting, dynamic payment adjustment logic, and parametric risk-sharing provisions within a unified decision support structure for bridge maintenance. The framework is calibrated using a 10-year dataset from 68 US bridges (2014–2023), incorporating inspection records, structural health monitoring (SHM) summaries, traffic loading, maintenance histories, and climate-exposure variables. A hybrid Markov–ML architecture is used to predict condition-state transitions and quantify uncertainty, and the resulting forecasts are translated into contract trigger points, adaptive maintenance responses, payment adjustments, and risk financing activation rules. The framework is evaluated through historical back-testing, scenario-based simulation, and sensitivity analysis of trigger thresholds, calibration settings, and risk-sharing configurations. Results indicate 91% overall predictive accuracy, with calibration error below 3% in the contract-relevant trigger region. Relative to the lump-sum baseline, the proposed APBMC framework reduces mean life-cycle maintenance cost by 22%, improves response time to critical repairs by 31%, and lowers major rehabilitation frequency by up to 18%. The integrated risk financing layer performs effectively under both condition-based triggers, with annual activation probabilities of 0.14–0.36, and flood-based triggers, with probabilities of 0.05–0.09, reducing the likelihood of unfunded major rehabilitation from 18% to below 5%. Sensitivity analysis further identifies stable operating regions for condition thresholds and model-mixing settings that improve cost certainty while maintaining resilience. The proposed APBMC framework demonstrates the value of coupling predictive analytics, adaptive contracting, and risk financing to support more resilient, transparent, and performance-oriented infrastructure maintenance governance.
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.
Ali Shehadeh, Odey Alshboul· Journal of Legal Affairs and...· 0 citations
An integrated framework uniting enterprise asset management, predictive analytics, and digital analytics for maintenance prioritisation and decision support is developed by developing an integrated framework uniting data preparation, feature engineering, modelling, evaluation, reliability translation, and decision integration.
Adeyemi Adebukunola Ishekwene· Journal of Engineering Resea...· 0 citations
The purpose of this study is to develop and validate a robust framework for data-driven predictive maintenance (PdM) that estimates the remaining useful life (RUL) of equipment and signals the optimal time to initiate maintenance activities. By integrating statistical modeling and machine learning techniques, the proposed framework aims to minimize unplanned downtimes, reduce maintenance costs and enhance operational efficiency. It addresses critical gaps in existing methods by providing real-time condition monitoring and predictive alerts, enabling maintenance personnel to make informed decisions and optimize maintenance scheduling.
This study introduces a data-driven predictive maintenance framework designed to estimate the RUL of equipment and provide timely signals for initiating maintenance tasks. The framework utilizes a combination of statistical modeling and machine learning algorithms. A Weibull distribution with time-varying parameters is employed to model the RUL, while random forest and exponentially weighted moving average (EWMA) control charts are integrated for failure prediction and maintenance signaling. The methodology is validated using a synthetic dataset that simulates real-world scenarios, enabling robust evaluation of the proposed approach in terms of accuracy and reliability.
The findings demonstrate that the proposed predictive maintenance framework effectively estimates the RUL of equipment with high accuracy and provides timely signals for initiating maintenance tasks. Validation on a synthetic dataset reveals that the framework consistently predicts failure probabilities and generates alerts well in advance, allowing sufficient time for maintenance planning. The integration of Weibull distribution modeling and Random Forest classifiers enhances the reliability of predictions, while the use of EWMA control charts ensures robust monitoring of failure probabilities. These results highlight the framework's potential to reduce downtimes and maintenance costs.
This study introduces a novel data-driven predictive maintenance framework for accurately estimating the RUL of equipment and providing timely signals for maintenance actions. Unlike existing methods, the proposed approach integrates a probabilistic model with machine learning techniques, leveraging time-varying Weibull distributions and advanced statistical tools for robust predictions. The framework not only improves failure prediction accuracy but also enhances maintenance planning by signaling appropriate times for action. This contributes to reducing downtime and costs while increasing operational efficiency, offering a significant advancement for Industry 5.0 and smart manufacturing applications.
Ahmad Razavi, M. R. Rasouli, M. Pishvaee et al.· Journal of Quality in Mainte...· 0 citations
This paper proposes an integrated framework for predictive asset management in electric power distribution systems by combining machine learning with the Analytic Hierarchy Process (AHP). Distinct utility datasets were used according to the characteristics of each prediction task. Annual preventive- and corrective-maintenance records, including maintenance cost and frequency, covered the period from 2016 to 2023, whereas the SAIDI and SAIFI annual time series covered the period from 2014 to 2023. Remaining Useful Life (RUL) estimation was based on static asset-level records from the entire available equipment stock and therefore was not associated with a single time-series interval. Multiple machine learning algorithms were evaluated independently for each Type of Utility Component (TUC), and the best-performing models were selected according to their validation errors. The resulting predictions were incorporated into a two-level AHP hierarchy that integrates technical, economic, operational, and regulatory criteria for portfolio-level asset-renewal prioritization. For RUL estimation, the selected TUC-specific models reduced the mean MAE from 11.33 years for the historical-mean baseline to 2.70 years, corresponding to an absolute improvement of 8.63 years and a relative reduction of 76.15%. The resulting priority ranking was evaluated retrospectively using moving-average, cumulative-gain, and lift analyses. The top 20% of ranked assets captured 29.61% of the observed corrective-maintenance occurrences, corresponding to a lift of 1.4806 relative to random selection. The cumulative-gain curve yielded an area under the curve of 0.6337 and a ranking Gini coefficient of 0.2674, indicating moderate positive discrimination across the asset portfolio. These results show that the proposed ML–AHP framework can support the allocation of inspection, engineering-assessment, and renewal resources toward assets with comparatively greater maintenance demand and operational relevance. The framework provides a structured, data-driven decision-support approach for utility asset-renewal planning, while prospective field implementation remains necessary to quantify reductions in failures, corrective maintenance, continuity penalties, and total expenditure.
Igor M. A. Santos, P. B. Vilar, Breno A. Vieira et al.· Energies· 0 citations
Preventive maintenance in a manufacturing plant is, at bottom, a resource-allocation problem: every intervention weighs the cost of replacing a component too early against the risk of an unplanned stoppage under the challenging realities of production calendars, crew availability, and machine access. Most facilities still rely on fixed calendar intervals that ignore what the equipment is signaling, paying for the difference in wasted component life and avoidable downtime. This paper describes a decision-support framework that connects data-driven prognostics to a production-constrained optimization model. A Gradient-Boosted Regression Tree (GBRT) supplies point estimates of Remaining Useful Life (RUL). A Quantile Regression Forest (QRF) turns these into calibrated prediction intervals with finite-sample coverage guarantees, and a Mixed-Integer Linear Program (MILP) converts the resulting per-machine failure probabilities into maintenance schedules that respect shift windows and crew capacity. The contribution lies less in the individual components than in how they are joined: uncertainty flows directly into the scheduler, so every intervention decision reflects both predictive risk and production feasibility. We evaluated the framework on a public milling benchmark and eight months of data from a twelve-machine packaging facility (ten independent runs each). Relative to the calendar-based policy currently used at the facility, total maintenance cost fell by 6.3% on the packaging line and 11.9% on the NASA milling dataset; unplanned downtime events fell by 7.2% and 44.4%; and unnecessary preventive replacements fell by 26.2% and 66.7%. Calibration error remained below 1.5 percentage points at every coverage level tested, and all scheduling instances were solved to certified optimality within seconds for fleets of up to 50 machines on standard hardware.
Hanfei Shi· Journal of Engineering, Proj...· 0 citations
Schedule delay remains a persistent challenge in large-scale infrastructure programmes, yet many predictive studies prioritise model accuracy while offering limited support for managerial interpretation and decision-making. This study aims to develop and evaluate a case-grounded, explainable hybrid machine-learning framework for schedule-delay risk prediction and decision-support translation.
The study integrates random forest and support vector machine within a weighted ensemble, with genetic algorithm optimisation applied to the random forest configuration. Shapley additive explanations is used to interpret model-attributed predictive contributions and identify non-linear risk-state patterns. The framework uses 470 case-grounded, programme-level risk-state observations derived from screened expert assessments and cross-checked against project documentation, practitioner interviews and schedule records.
The random forest-genetic algorithm + support vector machine model produced strong predictive results within the studied data set, achieving 92.2% accuracy with balanced precision, recall and F1-score values. The explainability analysis indicates that predicted schedule-delay risk is associated with non-linear, model-attributed risk-state patterns rather than isolated variables. Material and equipment supply, contractor performance and design changes emerged as the most salient predictive signals. These signals represent model-attributed contributions to predicted delay probability, not causal effects.
The study is based on a single large-scale infrastructure programme; therefore, broader transferability requires validation across multiple programmes, delivery systems and national contexts. The data are partly expert-informed, although screened and cross-checked against documentary and schedule evidence. SHAP-based explanations are model-attributed and should not be interpreted as causal effects. Future research should examine temporal modelling and benchmarking against XGBoost, LightGBM, CatBoost and deep-learning approaches.
The framework supports structured managerial prioritisation by translating model outputs into operational risk bands, trigger conditions, responsible actors, intervention timing and measurable outcomes.
By translating delay-risk predictions into explainable and trigger-based decision-support actions, the framework can support more transparent and accountable infrastructure project governance. Earlier identification of schedule-risk conditions may help reduce avoidable delays, resource waste, contractual disputes and disruption to public-service delivery. In large infrastructure projects, improved schedule-risk management can contribute to more reliable delivery of assets that affect communities, economic activity and public-sector investment efficiency. However, the framework should support, not replace, professional judgement and stakeholder accountability.
The study contributes by integrating hybrid prediction, explainable artificial intelligence and operational decision-support translation within a case-grounded infrastructure programme context. Rather than treating predictive accuracy or SHAP rankings as final outputs, the framework links predicted delay probability, dominant model-attributed risk signals and operational domains to trigger-based decision support.
Omid Tasa, M. Golabchi, M. Ravanshadnia· Journal of Engineering, Desi...· 0 citations