Optimization of Time, Cost, Energy, and Risk in Bridge Construction Projects Using Teaching-Learning-Based Optimization Under Integrated Project Delivery Framework

Abstract

Bridge construction projects are inherently complex and resource-intensive, demanding the careful balancing of multiple performance criteria such as time, cost, energy consumption, and construction risk. Traditional optimization approaches in construction management often focus solely on time-cost trade-offs, overlooking sustainability and safety objectives that are increasingly critical in contemporary infrastructure development. This study addresses this gap by formulating a discrete-time multi-objective optimization framework tailored specifically for bridge construction under the Integrated Project Delivery (IPD) model.

Introduction

Overview

• The construction industry, particularly bridge projects, plays a critical role in economic development and connectivity but faces challenges like complex interdependencies, safety risks, and sustainability concerns.

• Traditional project management focuses on time and cost, neglecting energy and risk, which results in sub-optimal outcomes.

• This study introduces a multi-objective optimization model using MOTLBO to address time, cost, energy consumption, and risk simultaneously.

???? Objectives of the Study

1. Formulate a multi-objective optimization model for bridge scheduling (time, cost, energy, risk).

2. Implement and tailor the MOTLBO algorithm to handle real-world construction constraints.

3. Support decision-making using a Weighted Sum Method (WSM), incorporating stakeholder preferences.

???? Literature Review Highlights

• Conventional methods (CPM, linear programming) struggle with real-world complexities.

• Metaheuristic algorithms (GA, PSO, ACO) and MOEAs (NSGA-II, NSGA-III) better handle multi-objective, nonlinear, and discrete construction problems.

• MOTLBO stands out due to:

  • Simpler structure

  • Less parameter tuning

  • Effective exploration/exploitation balance

?? Problem Formulation

Objective Functions:

• Minimize:

  1. Time – project duration

  2. Cost – activity execution cost

  3. Energy – kWh consumption (LCE-based)

  4. Risk – normalized risk index

Each activity has multiple execution modes with different time, cost, energy, and risk trade-offs.

???? MOTLBO Framework

• Inspired by classroom learning, with two phases:

  1. Teacher Phase – best solution (teacher) guides others.

  2. Learner Phase – peer-to-peer learning improves diversity.

• Uses non-dominated sorting to generate Pareto-optimal solutions.

• Key advantages:

  • Minimal parameter tuning

  • High solution diversity

  • Scalable for real-world scheduling

????? Case Study: Bridge Construction

• Project: Reinforced Concrete Girder Bridge, 300m long, INR 4.5 Cr, 18 months.

• Activities from Site Preparation to Handover.

• Each activity has 3 execution modes (Standard, Accelerated, Eco-Friendly), each with different performance values for time, cost, energy, and risk.

???? Implementation Details

• Platform: Python on HPC

• Optimized Parameters:

  • Population: 100

  • Iterations: 500

  • Teaching Factor: 1.5

  • Crossover: 0.8

  • Mutation: 0.05

• Stopping Criteria: Convergence at 10??

???? Results & Discussion

• 18 Pareto-optimal solutions generated.

• Solutions represent various trade-offs among time, cost, energy, and risk.

• Example:

  • S7 minimizes time (300 days) but has moderate cost and energy.

  • S4 minimizes risk (0.137) but takes 400 days.

  • S3 balances low energy (5146 kWh) and low risk (0.197).

???? Trade-off Analysis:

• Visual graphs reveal complex interdependencies:

  • Reducing time often increases risk or energy.

  • Eco-friendly modes usually lower energy but increase time or cost.

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. These findings reinforce the need for multi-objective optimization in construction management, especially for projects requiring a balance between efficiency, sustainability, and risk mitigation. By integrating energy and risk assessment into the traditional time-cost trade-off model, this research advances sustainable infrastructure planning and project scheduling. The MOTLBO framework enables construction stakeholders to make informed decisions based on real-world constraints, ensuring cost-effective, environmentally responsible, and risk-mitigated execution strategies.

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Copyright

Copyright © 2025 Ishan Raza Razvi , Dr. Mukesh Pandey, Dr. 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.