Multi-Objective Optimization of Time, Cost, Energy, and Risk in Bridge Construction Projects Using Teaching-Learning-Based Optimization

Abstract

The proposed model integrates four conflicting objectives—minimizing project duration, cost, energy consumption, and risk—into a unified decision-making platform. The Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is employed due to its superior convergence, solution diversity, and low parameter dependence. A real-world case study of a reinforced concrete girder bridge is used to validate the model, demonstrating its capability to generate 18 Pareto-optimal solutions across varied execution modes.

Introduction

As infrastructure projects become more complex and sustainability-focused, there is a growing need for advanced optimization frameworks that can balance multiple project objectives. This study proposes a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) model to optimize bridge construction projects under the Integrated Project Delivery (IPD) framework. The model aims to support low-carbon development, resilient infrastructure, and efficient public investment by simultaneously optimizing multiple project performance indicators.

The main research objectives include developing a multi-objective optimization model that considers project duration, cost, energy consumption, and construction risk. The MOTLBO algorithm is customized to solve the model efficiently and applied to a real reinforced concrete girder bridge project. The results are validated by comparing the algorithm’s performance with other optimization methods such as NSGA-II, NSGA-III, MOACO, and MOPSO, using evaluation metrics like Hypervolume (HV), Inverted Generational Distance (IGD), Spacing (Sp), and R² accuracy. A Weighted Sum Method (WSM) decision-support tool is also used to help stakeholders select the most balanced solution based on project priorities.

The literature review shows that previous studies mainly focused on time–cost optimization using algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and NSGA-II. However, only a few studies integrate energy consumption and risk along with time and cost in a single optimization framework.

The problem formulation addresses this gap by defining a multi-objective model that simultaneously optimizes time, cost, energy use, and risk in bridge construction scheduling.

The proposed MOTLBO framework uses a teaching–learning mechanism that enables faster convergence and higher computational efficiency compared to traditional evolutionary algorithms. It also supports discrete decision-making for selecting construction execution modes, making it suitable for complex large-scale construction projects.

A case study was conducted on a 300-meter reinforced concrete girder bridge with six spans, including multiple construction activities such as site preparation, excavation, piling, pier construction, deck slab installation, and finishing works. Each activity has three execution modes—standard, accelerated, and eco-friendly—with different values for time, cost, energy consumption, and risk.

The optimization process produced 18 Pareto-optimal solutions, representing different trade-offs between the four objectives. The results show that the MOTLBO algorithm effectively identifies balanced solutions that help decision-makers choose the most efficient and sustainable construction strategy.

Conclusion

This study developed a discrete time-cost-energy-risk optimization framework for bridge construction projects under the IPD approach using the MOTLBO algorithm. By simultaneously optimizing four conflicting objectives—minimizing project duration, reducing costs, lowering energy consumption, and mitigating construction risk—the framework provides a robust decision-support tool for infrastructure project managers. The case study on a reinforced concrete girder bridge validated the model by generating Pareto-optimal solutions, demonstrating how different execution strategies impact overall project efficiency. The trade-off analysis revealed that accelerated schedules increase energy consumption and risk, whereas eco-friendly execution modes promote sustainability but extend project duration. Correlation analysis further confirmed interdependencies among objectives, particularly the inverse relationship between time and energy and the positive correlation between cost and risk mitigation. The comparative analysis with NSGA-II, NSGA-III, MOACO, and MOPSO demonstrated that MOTLBO outperformed traditional algorithms, achieving superior solution diversity, convergence accuracy, and computational efficiency.

References

[1] Agarwal, A. K., Chauhan, S. S., Sharma, K., & Sethi, K. C. (2024). Development of time–cost trade-off optimization model for construction projects with MOPSO technique. Asian Journal of Civil Engineering, 0123456789. https://doi.org/10.1007/s42107-024-01063-3 [2] Behera, A. P., Dhawan, A., Rathinakumar, V., Bharadwaj, M., Rajput, J. S., & Sethi, K. C. (2024). Optimizing time, cost, environmental impact, and client satisfaction in sustainable construction projects using LHS-NSGA-III: a multi-objective approach. Asian Journal of Civil Engineering, 0123456789. https://doi.org/10.1007/s42107-024-01221-7 [3] Kaveh, A., Bakhshpoori, T., & Hamze-Ziabari, S. M. (2018). M5’ and mars based prediction models for properties of selfcompacting concrete containing fly ash. Periodica Polytechnica Civil Engineering, 62(2), 281–294. https://doi.org/10.3311/PPci.10799 [4] Li, M., & Wu, G. (2014). Robust optimization for time-cost tradeoff problem in construction projects. Abstract and Applied Analysis. https://doi.org/10.1155/2014/926913 [5] Prasad, A., Mayank, B., Gaurav, C., Prachi, S., & Jyoti, S. (2024). Optimizing trade ? off between time , cost , and carbon emissions in construction using NSGA ? III?: an integrated approach for sustainable development. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-024-01176-9 [6] Sethi, K. C., Rathinakumar, V., Harishankar, S., Bhadoriya, G., & Pati, A. K. (2024). Development of discrete opposition-based NSGA-III model for optimizing trade-off between discrete time, cost, and resource in construction projects. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-024-01069-x

Copyright

Copyright © 2026 Shreyash Sachan, Mukesh Pandey, Rakesh Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.