International Journal of Optimization in Civil Engineering

OPTIMAL DESIGN OF REGULAR DIAGRID SYSTEMS WITH DISCRETE VARIABLES UNDER DIFFERENT GEOMETRIES AND BOUNDARY CONDITIONS

M. Shahrouzi

Diagrids are of practical interest in high-rise buildings due to their architectural configuration and efficiency in withstanding lateral loads by exterior diagonal members. In the present work, diagrid models are screened based on a sizing optimization approach. Section index of each member group is treated as a discrete design variable in the optimization problem to be solved. The structural constraints are evaluated due to Load and Resistant Design Factor regulations under both gravitational and wind loadings. The research is threefold: first, falcon optimization algorithm is utilized as a meta-heuristic paradigm for such a large-scale and highly constrained discrete problem. Second, the effect of geometry variation in diagrids on minimal structural weight is studied for 18 diagrid models via three different heights (12, 20 and 30 stories) and three diagrid angles. Third, distinct cases of rigid and flexible bases are compared to study the effect of such boundary conditions on the results. The effect of soil flexibility beneath the foundation on the optimal design was found highly dependent on the diagrid geometry. The best weight and performance in most of the treated examples belong to the geometry that covers two stories by every grid line on the flexible-base.

A COMPARATIVE STUDY OF SINGLE-OBJECTIVE AGAINST MULTI-OBJECTIVE OPTIMIZATION IN STRUCTURAL

S. TalatAhari

Structural optimization plays a critical role in improving the efficiency, cost-effectiveness, and sustainability of engineering designs. This paper presents a comparative study of single-objective and multi-objective optimization in the structural design process. Single-objective problems focus on optimizing just one objective, such as minimizing weight or cost, while other important aspects are treated as constraints like deflections and strength requirements. Multi-objective optimization addresses multiple conflicting objectives, such as balancing cost, with displacement treated as a secondary objective and strength requirements defined as constraints within the given limits. Both optimization approaches are carried out using Chaos Game Optimization (CGO). While single-objective optimization produces a definitive optimal solution that can be used directly in the final design, multi-objective optimization results in a set of trade-off solutions (Pareto front), requiring a decision-making process based on design codes and practical criteria to select the most appropriate design. Through a real-world case study, this research will assess the performance of both optimization strategies, providing insights into their suitability for modern structural engineering challenges.

SEISMIC CONTROL OF MAGNETO-RHEOLOGICAL DAMPER-EQUIPPED STRUCTURES USING SLIDING SECTOR CONTROL

M. Nikpey

In recent years, semi-active control has been introduced as a promising method for the seismic control of structures, potentially combining the benefits of both passive and active control systems. Magneto-rheological damper (MR) is one of the semi-active devices and its dynamic model is expressed by the Bouc-Wen model. The sliding sector control (SSC) strategy as a robust control approach is a class of variable structure (VS) systems for linear and nonlinear continuous-time systems with a special type of sliding sector using a new equivalent sector control. The purpose of this study is to evaluate the effectiveness of the SSC strategy in determining the optimal voltage of MR at each step of time. For a numerical example, a three-story benchmark shear structure is considered subjected to normal (100%), high (150%), and low (50%) excitation levels of the El Centro earthquake. The results of the numerical simulations show that the semi-active control system consisting of the SSC strategy and an MR damper can be beneficial in reducing the seismic responses of structures. Furthermore, the efficiency of the SSC strategy is also compared against that of the fuzzy and clipped-optimal controllers. Comparative results of the numerical simulation confirm the robustness and ability of the SSC strategy.

MATERIAL COST OPTIMIZATION OF ONE-WAY REINFORCED CONCRETE SLABS USING AN ELITIST GENETIC ALGORITHM: A SENSITIVITY ANALYSIS BASED ON ACI 318-19

B. Ahmadi-Nedushan

This study uses an elitist Genetic Algorithm (GA) to optimize material costs in one-way reinforced concrete slabs, adhering to ACI 318-19. A sensitivity analysis demonstrated the critical role of elitism in GA performance. Without elitism, the GA consistently failed to reach the target objective, with success rates often nearing zero across various crossover fractions. Incorporating elitism dramatically increased success rates, highlighting the importance of preserving high-performing individuals. With an optimal configuration of 0.3 crossover fraction and 0.45 elite percentage, a 92% success rate was achieved, finding a cost of 24.91 in 46 of 50 runs for a simply supported slab. This optimized design, compared to designs based on ACI 318-99 and ACI 318-08, yielded material cost savings of between 5.8% to 8.6% for simply supported, one-end continuous, both-ends continuous, and cantilevered slabs. The influence of slab dimensions on cost was evaluated across 64 scenarios, varying slab lengths from 5 to 20 feet for each support condition. Resulting cost versus slab length diagrams illustrate the economic benefits of GA optimization.

OPTIMIZATION OF TOWER CRANES AND SUPPLY POINTS LOCATION USING MBF ALGORITHM

D. Sedaghat Shayegan

Tower cranes are essential for both vertical and horizontal movement of materials in construction and port operations. Optimizing their placement is crucial for reducing costs and enhancing overall efficiency. This study addresses the optimization of tower crane placement using the recently developed Mouth Brooding Fish (MBF) algorithm. The MBF algorithm is inspired by the life cycle of mouth-brooding fish, employing their behavioral patterns and the survival challenges of their offspring to find optimal solutions. The performance of the MBF algorithm is compared with the Genetic Algorithm (GA), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO). The results demonstrate that the MBF algorithm is effective and has potential advantages in tackling complex optimization problems.

MULTI-MATERIAL OPTIMAL DESIGN OF 3D TRANSMISSION TOWERS USING BLACK HOLE MECHANICS OPTIMIZATION: REAL-SIZE EXAMPLES

P. Salmanpour

The multi-material size optimization of transmission tower trusses is carried out in the present study. Three real-size examples are designed, and statically analyzed, and the Black Hole Mechanics Optimization (BHMO) algorithm, a recently developed metaheuristic optimizer methodology, is employed. The BHMO algorithm's innovative search strategy, which draws inspiration from black hole quantum physics, along with a robust mathematical kernel based on the covariance matrix between variables and their associated costs, efficiently converges to global optimum solutions. Besides, three alloys of steel are taken into account in these examples for discrete size variables, each of which is defined in the problem by a weighted coefficient in terms of the elemental weight. The results also indicate that using multiple materials or alloys in addition to diverse cross-sectional sizes leads to the lowest possible cost and the most efficient solution.

SEISMIC CONFIDENCE LEVELS AND COLLAPSE CAPACITY ASSESSMENT OF STEEL MOMENT RESISTING FRAMES USING NEURAL NETWORKS

S. Gholizadeh

This paper employs neural network models to assess the seismic confidence levels at various performance levels, as well as the seismic collapse capacity of steel moment-resisting frame structures. Two types of shallow neural network models including back-propagation (BP) and radial basis (RB) models are utilized to evaluate the seismic responses. Both neural network models consist of a single hidden layer with a different number of neurons. The prediction accuracy of the trained neural network models is compared using two illustrative examples of 6- and 12-story steel moment-resisting frames. The obtained numerical results indicate that the BP model outperforms the RB model in predicting seismic responses.

NODAL ORDERING OF GRAPHS FOR WAVEFRONT OPTIMIZATION USING NEURAL NETWORK AND WATER STRIDER ALGORITHMS

A. Kaveh

In this paper, a neural network is trained for optimal nodal ordering of graphs to obtain a small wavefront using soft computing. A preference function consists of six inputs that can be seen as a generalization of Sloan's function. These six inputs represent the different connection characteristics of graph models. This research is done with the aim of comparing Sloan's theoretical numbering method with Sloan's developed method with neural networks and WSA meta-heuristic algorithm. Unlike the Sloan algorithm, which uses two fixed coefficients, six coefficients are used here, based on the evaluation of artificial neural networks. The weight of networks is obtained using Water Strider algorithm. Examples are included to demonstrate the performance of the present hybrid method.