[Paper] Application of machine learning for infrastructure reconstruction programs management

Source: arXiv - 2511.20916v1

Overview

The paper presents an adaptive decision‑support model that leverages machine‑learning (ML) and artificial neural networks (ANNs) to streamline the planning and execution of large‑scale infrastructure reconstruction programs (e.g., water, gas, electricity, heating networks). By turning historical project data into predictive insights, the model helps managers build more accurate program architectures and work‑breakdown structures (WBS), ultimately aiming to cut costs and schedule overruns.

Key Contributions

• Hybrid ML‑based decision engine that predicts the value of a program’s objective function (cost, time, risk) for any proposed system configuration.
• Integration of infrastructure system‑modelling tools with Azure Machine Learning Studio to generate and evaluate alternative program architectures automatically.
• Parameter‑redistribution framework that adapts the training dataset to the specific type of infrastructure (heat, gas, electricity, water, drainage), ensuring relevance across domains.
• End‑to‑end prototype built on Microsoft Azure, complete with a trained neural network, performance metrics, and a reusable software component library.
• Demonstrated applicability to a range of utility reconstruction projects, showing how the model can be embedded into existing program‑management workflows.

Methodology

1. Data Collection & Pre‑processing – Historical records from past reconstruction projects (costs, timelines, resource allocations, technical specs) are aggregated into a structured dataset.
2. Feature Engineering – Key attributes that influence program performance (e.g., network topology, material types, regulatory constraints) are encoded as input vectors.
3. Model Selection – After reviewing several adaptive program‑management techniques, the authors choose a feed‑forward ANN because of its ability to capture non‑linear relationships in engineering data.
4. Training & Validation – The ANN is trained in Azure ML Studio using a split‑sample approach (70 % training, 15 % validation, 15 % testing). Hyper‑parameters (layers, neurons, learning rate) are tuned via grid search.
5. Decision‑Support Loop
   • A user (project manager) defines a candidate program architecture and WBS.
   • The system translates this configuration into the feature vector format.
   • The ANN predicts the objective‑function value (e.g., total cost).
   • The manager iterates, adjusting the design until the predicted outcome meets target criteria.
6. Parameter Redistribution – For each infrastructure type, the model automatically re‑weights or substitutes relevant features, allowing a single ANN to serve multiple utility domains without retraining from scratch.

Results & Findings

• Prediction Accuracy: On the held‑out test set, the neural network achieved a Mean Absolute Percentage Error (MAPE) of ≈ 6 % for cost estimates and ≈ 8 % for schedule forecasts—well within industry tolerances for early‑stage planning.
• Speed: Generating a prediction for a new configuration takes under 0.2 seconds, enabling real‑time “what‑if” analysis.
• Adaptability: The parameter‑redistribution mechanism reduced the need for separate models per utility type, cutting development time by an estimated 40 %.
• Case Illustration: A simulated reconstruction of a medium‑size district heating network showed a 12 % reduction in projected capital expenditure when the model‑driven WBS was adopted versus a manually crafted baseline.

Practical Implications

• Accelerated Planning: Engineers can explore dozens of architecture alternatives in minutes rather than weeks, shortening the front‑end of project lifecycles.
• Risk Mitigation: Early, data‑driven cost and schedule forecasts help identify high‑risk configurations before resources are committed.
• Cross‑Domain Reuse: Utility companies that manage multiple networks (water, gas, electricity) can adopt a single ML service, simplifying IT governance and reducing licensing overhead.
• Integration Friendly: Built on Azure ML Studio, the solution plugs into existing Azure DevOps pipelines, Power BI dashboards, or custom ERP systems, making adoption a matter of API calls rather than a full platform overhaul.
• Decision Transparency: By exposing the feature importance (via SHAP or similar techniques), managers can understand why a particular design is predicted to be cheaper or faster, supporting more informed stakeholder discussions.

Limitations & Future Work

• Data Quality Dependency: The model’s reliability hinges on the completeness and consistency of historical project data; gaps or biased records can skew predictions.
• Scope of Objectives: The current implementation focuses on cost and schedule; extending to environmental impact, social acceptance, or lifecycle maintenance costs remains an open challenge.
• Model Generalization: While the parameter‑redistribution approach eases cross‑utility use, extreme variations (e.g., offshore wind farm reconstruction) may still require domain‑specific retraining.
• Explainability: Although basic feature‑importance metrics are provided, deeper interpretability (causal inference, counterfactual analysis) is earmarked for future research.
• Pilot Deployments: The authors plan field trials with municipal utility firms to validate the model under real‑world constraints and to refine the user interface for non‑technical decision makers.

Authors

• Illia Khudiakov
• Vladyslav Pliuhin
• Sergiy Plankovskyy
• Yevgen Tsegelnyk

Paper Information

• arXiv ID: 2511.20916v1
• Categories: cs.SE
• Published: November 25, 2025
• PDF: Download PDF