A TLBO-Based Approach for Optimizing Time, Cost, Energy and Risk in Bridge Construction under the Integrated Project Delivery Model

Abstract

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. 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 (standard, accelerated, eco-friendly).

Introduction

The construction industry, especially bridge construction, is vital for national development due to its impact on mobility, regional integration, and economic growth. However, bridge projects are complex and resource-intensive, involving risks, fluctuating constraints, and sustainability concerns.

Traditional project management has focused narrowly on cost and duration, often neglecting sustainability, energy efficiency, and risk. This study addresses these limitations by employing a multi-objective optimization (MOO) approach to better balance time, cost, energy, and safety in bridge construction.

2. Focus of the Study

The research optimizes a reinforced concrete girder bridge project composed of 12 core activities, each with three execution modes:

• Standard

• Accelerated (shorter duration, higher energy and risk)

• Eco-friendly (lower environmental impact, longer duration)

Using the Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm, the study generates a Pareto front of optimal trade-offs, supporting informed decision-making aligned with project priorities.

3. Research Objectives

1. Apply the MOTLBO algorithm to a real bridge project to identify optimal trade-offs.

2. Validate MOTLBO's performance by comparing it with other MOO algorithms:

   • NSGA-II

   • NSGA-III

   • MOPSO

   • MOACO

4. Literature Review Highlights

A. Complexity in Construction Projects

• Construction projects involve interdependent tasks, resource conflicts, and multiple stakeholders.

• Objectives like cost, time, safety, and sustainability often conflict.

B. Shift to Multi-Objective Optimization

• Traditional methods (linear programming, CPM) lack flexibility for nonlinear, dynamic problems.

• Metaheuristic algorithms (e.g., GA, PSO, ACO) offer better adaptability.

• MOTLBO stands out for:

  • Simplicity

  • Minimal parameter tuning

  • Fast convergence

  • Effective handling of discrete scheduling problems

C. Time-Cost Trade-Off (TCTO) Models

• Traditional models focused only on time and cost.

• Metaheuristics like NSGA-II produce Pareto fronts showing multiple solutions rather than a single optimum.

• Hybrid models (e.g., PSO-GA, Fuzzy MOGA) integrate uncertainty and improve decision accuracy.

D. Importance of MOO in IPD (Integrated Project Delivery)

• Modern project environments require optimization that reflects external factors, like penalties and procurement delays.

• MOO supports adaptive and transparent decision-making.

5. Proposed Method: MOTLBO Algorithm

Overview:

MOTLBO mimics classroom learning and involves two phases:

1. Teacher Phase – Best solution (teacher) improves the population.

2. Learner Phase – Solutions learn from each other and evolve.

Key Steps:

1. Problem Initialization – Define activities, constraints, and execution modes.

2. Population Initialization – Assign decision variables and establish objective functions for time, cost, energy, and risk.

3. Teacher Phase – The top solution influences others toward better performance.

4. Learner Phase – Solutions iteratively improve through peer comparison.

5. Non-Dominated Sorting – Identify optimal solutions (Pareto front) and maintain diversity.

6. Termination & Selection – Algorithm stops when goals are met. Final solution is chosen using tools like Weighted Sum Method (WSM) based on stakeholder preferences.

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 © 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.