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

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

The construction industry plays a crucial role in economic development, and bridge projects require efficient planning due to their complexity, resource demands, safety concerns, and environmental impact. Traditional construction optimization mainly focuses on reducing time and cost while ignoring factors such as energy consumption, sustainability, and risk. This research proposes a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) framework to optimize bridge construction by balancing four conflicting objectives: project duration, cost, energy consumption, and risk.

The study focuses on a 300-meter reinforced concrete girder bridge consisting of 12 major construction activities. Each activity has three execution modes: standard, accelerated, and eco-friendly, representing different trade-offs. Accelerated methods reduce completion time but increase cost, energy usage, and risk, while eco-friendly methods reduce environmental impact but may increase project duration. The MOTLBO algorithm is applied to generate a set of Pareto-optimal solutions, helping decision-makers select the best strategy based on project priorities.

The research reviews existing optimization approaches such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), NSGA-II, and NSGA-III. Traditional techniques like linear programming and critical path methods are limited in handling complex construction problems, whereas metaheuristic algorithms provide better flexibility and global optimization capabilities. MOTLBO improves upon these methods by using a teacher-student learning concept, requiring fewer parameters and providing strong convergence for construction scheduling problems.

The proposed MOTLBO framework works through several stages:

• Defining construction activities, constraints, and objectives.
• Generating an initial population of possible solutions.
• Applying a Teacher Phase where the best solution guides others.
• Applying a Learner Phase where solutions improve through peer interaction.
• Using non-dominated sorting to create Pareto-optimal solutions.
• Selecting the most suitable solution using decision-making methods such as the Weighted Sum Method.

The bridge project includes activities such as site preparation, foundation excavation, piling, pier construction, deck installation, pavement work, inspection, and handover. Each activity is evaluated based on time, cost, energy consumption, and risk values. The optimization process generated 18 Pareto-optimal solutions, showing different combinations of construction strategies.

The results demonstrate that MOTLBO can effectively identify balanced solutions between competing objectives. The generated solutions provide alternatives ranging from faster completion with higher costs and risks to environmentally sustainable options with lower energy consumption. The average optimized values obtained were approximately:

• Project duration: 360.7 days
• Cost: INR 4.45 crore
• Energy consumption: 7154.8 kWh
• Risk level: 0.327

Conclusion

The research focused on developing a robust multi-objective optimization framework to address the multifaceted challenges encountered in bridge construction projects, particularly under the Integrated Project Delivery (IPD) framework. Using the Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm, the study simultaneously optimized time, cost, energy consumption, and risk—four critical yet often conflicting objectives. The chapter outlines the achievement of these objectives, provides conclusive answers to the research questions, summarizes the findings, and offers final reflections along with directions for future research.

References

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Copyright

Copyright © 2026 Prashant Sharma, Rakesh Gupta, Mukesh Pandey. 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.