International Journal of Optimization in Civil Engineering

PREDICTION OF MODAL STRAIN ENERGY USING MULTI-OBJECTIVE METAHEURISTIC-TRAINED ARTIFICIAL NEURAL NETWORKS

V. R. Mahdavi

This paper uses multi-objective methods to improve the exploration for single-objective problems. This method involved splitting the objective function into two segments and these enhanced using specialized algorithms designed for handling multiple objective functions. Three MO algorithms include Colliding Bodies Optimization (MOCBO), Particle Swarm Optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II), which are used to get the best prediction of structural modal strain energy. The indicated method is then implemented to two spatial truss structures. The recently developed method has superior performance over the previous approach that relied on single-objective optimization (SO) algorithms.

ADVANCES IN OPTIMIZATION OF DAMS: A STATE OF THE ART REVIEW

S. Chhetri

Optimization strategies have turned out to be a requirement in the design and construction of dams because of the increasing demand for water resources, hydropower generation, and flood control and due to the inbuilt high cost and complexities of such massive structures (1) (2) (3) (4) (5) (6) (7). This review paper summarizes recent advances on dam optimization, covering a broad scope of dam types, primary objectives, approaches, and performance evaluation indices. It highlights the vast transition from traditional, at times time-consuming, trial-and-error based design techniques and classical optimization methods to efficient meta-heuristic (MH) and hybrid algorithms that outperform in terms of efficiency, accuracy, and global exploration (1) (3) (8). The paper also touches upon the growing application of Reliability-Based Design Optimization (RBDO) and robust optimization towards dealing with material property uncertainty, loading, and environmental uncertainties to achieve safer and cost-efficient designs (9) (10) (11). Ideal optimization objectives, such as volume reduction of concrete and dam safety maximization, are explored along with several geometric, stress, stability, and frequency constraints. Finally, it discusses the ongoing problems, including the computational expense and selection of appropriate algorithms, and instilling future research directions essential for further advancement of dam engineering practice.

OPTIMUM VIBRATION CONTROL OF OFFSHORE WIND TURBINES USING MTMD UNDER SIMULTANEOUS EFFECT OF WIND AND WAVE LOADS

M. Fahimi Farzam

Considering the depletion of fossil fuel resources and their environmental impacts, offshore wind energy has emerged as a promising renewable solution. However, floating offshore wind turbines (FOWTs) are exposed to complex aerodynamic and hydrodynamic loads, particularly under combined wind and wave excitation, which can lead to excessive vibrations and fatigue damage. Therefore, effective vibration control strategies are essential to enhance structural reliability and durability. This study investigates the dynamic behavior and vibration mitigation of a floating offshore wind turbine supported by a Tension Leg Platform (TLP) under moderate, high, and extreme wind-wave conditions. A coupled model was developed using FAST-SC, and Multi-Tuned Mass Damper (MTMD) systems were implemented as passive control devices. The optimal MTMD parameters and installation layouts were determined, and their effectiveness in reducing tower and platform responses was evaluated. The results demonstrate that the optimized MTMD systems significantly improve the dynamic performance of the FOWT, achieving maximum reductions of 34% in tower fore-aft displacement, 43% in platform roll motion, and 41% in tower-base bending moment compared with the uncontrolled case. These reductions indicate the capability of MTMD systems to suppress structural vibrations, mitigate fatigue accumulation, and enhance the long-term reliability of offshore wind turbines. Consequently, the proposed approach has the potential to extend the service life of critical structural components and reduce maintenance requirements and life-cycle costs. This study provides valuable insights for the design of reliable and durable floating offshore wind turbines operating in harsh marine environments.

OPTIMAL DESIGN OF DUAL STEEL FRAME STEEL SHEAR WALL SYSTEMS USING A LEVY FLIGHT ENHANCED PUMA OPTIMIZATION ALGORITHM

H. Azizian

The dual steel system comprising moment-resisting frames integrated with steel shear walls represents an advanced seismic-resistant solution in structural engineering. This system synergistically combines the high ductility and energy dissipation capacity of steel frames with the substantial lateral stiffness and strength provided by steel shear walls. Proper design of such systems requires precise determination of the optimal location, thickness, and mechanical properties of the shear walls parameters that critically govern seismic performance, structural safety, material efficiency, and construction cost. To achieve an optimal balance between performance and economy, the problem is formulated as a constrained optimization task. This study employs the recently developed Puma Optimizer (PO) and introduces a novel enhanced variant, termed the Upgraded Puma Optimizer (U-PO). The key novelty of this work lies in the integration of Lévy flight distribution into the PO framework, replacing conventional Brownian motion to significantly strengthen the exploration–exploitation balance, global search capability, and convergence speed. This modification enables more effective handling of complex, high-dimensional structural optimization problems. The performance of the proposed U-PO is rigorously evaluated through the optimal design of three benchmark steel frames (1-, 10-, and 20-story) equipped with shear walls. The primary objective is to minimize the total structural weight while satisfying strength, serviceability, and seismic design requirements according to relevant building codes. Decision variables include frame member cross-sections as well as the location and thickness of shear walls. Comparative results against several established metaheuristic algorithms (HHO, AOA, and GWO) demonstrate the superiority of the U-PO, confirming that the incorporation of Lévy flights leads to markedly improved convergence behavior and solution quality. The U-PO consistently yields superior designs featuring notable reductions in structural weight through more efficient sizing and strategic placement of steel shear walls.

STRUCTURAL DAMAGE IDENTIFICATION USING THE FINITE ELEMENT MODEL UPDATING METHOD BASED ON STATIC RESPONSES OF THE STRUCTURE

A. Bagherkhani

Detecting the location and severity of damage, particularly in the early stages, is a significant topic in structural engineering. This study proposes a new identification method that can achieve third-level damage detection of structures. The approach involves conducting a static load test to measure the structure's static responses, along with a finite element model updating method that utilizes an optimization strategy. Based on the structure's static responses, a new damage index is introduced. This index comprises two components: a location index, which represents the residual values between structural responses in intact and damaged states, and a severity index, which is defined based on the correlation coefficient between structural responses in actual damage scenarios and the outcomes from the model updating process. Jaya optimization algorithm is used in this regard. The effectiveness of the proposed method is assessed through four numerical examples involving beam and frame structures under various damage scenarios. In addition, the method's performance is evaluated for noisy data. The results demonstrate the proposed method's effectiveness and high precision in identifying structural damage. However, the results also indicate that the proposed approach is highly sensitive to noisy data.

APPLICATION OF THE MOUTH BROODING FISH ALGORITHM FOR CONSTRUCTION SITE LAYOUT OPTIMIZATION IN INDUSTRIAL PROJECTS

D. Sedaghat Shayegan

Deciding where to place temporary facilities on a construction site is one of the more consequential choices a project team makes, since that arrangement shapes how materials and people move around the site, how accessible different zones are, and how smoothly logistics run overall. This paper examines whether the Mouth Brooding Fish (MBF) algorithm can handle a standard construction site layout planning problem. The goal is to minimize the total distance that must be traveled between facilities that interact with each other. A well-known benchmark involving eleven facilities and eleven predetermined locations is solved with MBF and compared against Genetic Algorithm (GA), Ant Colony Optimization (ACO), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO). Fifty independent runs are performed, each with 200 iterations. The best solution obtained by MBF yields a total travel distance of 12,538, while the mean, worst, and standard deviation values are 12,608, 12,888, and 96, respectively. These figures place MBF in a competitive position relative to the other methods on this particular benchmark. The study does not claim direct field validation for oil and gas projects; rather, it demonstrates that the optimization framework works on a benchmark whose structure is relevant to industrial construction logistics. Further work that incorporates project-specific constraints and actual field data is recommended.

A CHAOTIC QUANTUM-INSPIRED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMAL DESIGN OF LARGE-SCALE STRUCTURES

H. Fazli

Large-scale structural sizing optimization involves discrete design variables, nonlinear constraints, and repeated computationally expensive structural analyses. This study proposes a Chaotic Quantum-Inspired Particle Swarm Optimization (CQPSO) algorithm that integrates chaotic search mechanisms into QPSO to improve exploration, population diversity, and convergence. The proposed method is evaluated on a range of planar and spatial truss and frame benchmarks and compared with PSO, QPSO, chaotic PSO, CLPSO, differential evolution, and Grey Wolf Optimizer. Five chaotic maps are systematically examined within the CQPSO framework. The results show that CQPSO generally provides lower structural weight, greater robustness, and higher feasibility than the reference methods. The tent map provides the most favorable overall performance, with convergence and diversity analyses indicating an improved balance between exploration and exploitation. Statistical tests confirm that the observed improvements are significant for most comparisons, while computational results show that CQPSO reaches competitive designs with fewer structural evaluations and lower CPU time. The proposed approach therefore provides an effective and computationally efficient framework for discrete structural sizing optimization.