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The rise in percentage of deteriorated bridges coupled with the escalation of maintenance costs call for systematic bridge management systems. Therefore, this research paper introduces an exponential chaotic differential evolution (ECDE) model for optimizing bridge maintenance plans at both project and network levels. The developed optimization model is envisioned on addressing the performance, economic, social, and environmental aspects of bridge maintenance management. In addition, it exploits the use of chaotic sequences to circumvent the critical shortcomings of local minima entrapment and premature convergence encountered by classical optimization metaheuristics. The validity of the developed model is experimented using a comparative analysis of a wide set bridge elements and over a varying multi‐year maintenance plan. Comparison results reveal that ECDE‐based Sinusoidal algorithm improve the performance diagnostics of classical meta‐heuristics by values ranging from 49.2% to 73.1% over the multi‐year maintenance plans. It can be argued that the developed model could benefit transportation agencies in formulating flexible, sustainable, and cost‐effective maintenance strategies in addition to preserving the structural condition of designated bridges in the network. [ABSTRACT FROM AUTHOR]

Waste glass (WG) can be used as fine aggregate and powder in concrete mixtures, preventing pollution induced by this non‐biodegradable material. The properties of WG‐included concrete should be examined before its practical use. Compressive strength (CS) is one of the most crucial characteristics of concrete, and the measurement of which needs time‐consuming and expensive experiments. The use of machine learning (ML) methods for modeling the CS of concrete can help achieve more reliable and precise models. In this study, a comprehensive database of WG‐included concrete was collected from the literature. Next, four ML methods, including support vector regression (SVR), least‐square support vector regression (LSSVR), adaptive neuro‐fuzzy inference system (ANFIS), and multilayer perceptron neural network (MLP) were served in the CS modeling. A recently proposed metaheuristic method, called marine predators algorithm (MPA), was proposed to optimize the control parameters of the ML models to guarantee generalized accuracy. Results indicate that the hybrid LSSVR‐MPA model outperforms the other developed ML models comparing the error metrics with an RMSE = 2.447 MPa and R2 = 0.983. The sensitivity analysis reveals that replacing the cement with WG powder decreases the CS, whereas serving the WG as the replacement for natural fine aggregate improves the CS. [ABSTRACT FROM AUTHOR]

This study presents an advanced framework for modeling the lateral-torsional buckling behavior of cellular steel beams, which combines hybrid intelligent models with numerical simulation. The proposed hybrid intelligent models employ a large dataset-based finite element method (FEM) for training and validation the framework, as well as metaheuristic algorithms for optimal auto-hyper-parameters selection. A total of 1535 numerical models are examined in order to evaluate the lateral-torsional buckling behavior. Following that, the least square support vector machine (LSSVM) optimized using four metaheuristic algorithms (ME): particle swarm optimization (PSO), ant lion optimization (ALO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO) algorithms, is utilized to estimate accurately the lateral-torsional buckling resistance. According to the findings of a comprehensive performance evaluation utilizing statistical and graphical comparing criteria, the suggested LSSVM-ME predicts the lateral-torsional buckling behavior with excellent accuracy. LSSVM-HHO, in particular, outperforms the other hybrid intelligence models, with an RMSE of 41.72 kN.m and an NSE of 0.99. Overall, the results indicate that the proposed framework has a great potential for use as a practical tool for estimating the lateral-torsional buckling behavior of cellular steel beams. [ABSTRACT FROM AUTHOR]

• An improved directional simulation is developed using meta -heuristic algorithms. • Harris Hawks Optimization is integrated to determine the sensitivity vector. • Efficiency of reliability analysis is conducted against several reliability methods. • Results indicate a strong adaptation for solving highly nonlinear functions. • The proposed method estimate the reliability index with low computational cost. In this paper, a new framework for accurate reliability analysis is proposed based on improving the directional simulation by using metaheuristic algorithms. Usually for highly nonlinear and complex performance functions, finding the unit vector direction requires very high calculations or impossible practically. Hence, the novel improved version incorporates the Harris Hawks Optimization algorithm, where the unit vector of direction is formulated as a constrained optimization problem and estimated using metaheuristic algorithms. Given that metaheuristic algorithms have been introduced to solve unconstrained problems, the penalty function method is used to convert the constrained problem into an unconstrained problem. The applicability of the proposed framework is firstly tested on five highly nonlinear benchmark functions and then applied to solve four high-dimensional engineering problems. The performance of six simulations-based reliability analysis methods and the first-order reliability method were compared with the proposed method. Besides the feasibility of other metaheuristic algorithms were investigated. The results show high-performance abilities of the improved version of the directional simulation for solving highly nonlinear engineering problems. [ABSTRACT FROM AUTHOR]

Computing the sensitivity vector in the traditional first order reliability method may provide inaccurate reliability outcomes for discrete performance functions and inefficient computation burden for high-dimensional problems. In this study, two improved particle swarm optimization algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis. The abilities for convergence speed and global convergence of the particle swarm optimization algorithm are improved using a novel hybrid method called particle swarm optimization-based harmony search algorithm (PSO–HS), and enhanced particle swarm optimization (EPSO). The proposed methods use a dynamic self-adaptive term to execute the local adjusting process. Using twelve numerical-based engineering problems, the structural reliability frameworks developed based on modified versions of particle swarm optimization algorithms are compared to numerous FORM algorithms and the current metaheuristic methods. Results indicated that the novel proposed methods using the improved PSO algorithms are more robust and efficient than the analytical FORM methods for solving high-dimensional engineering problems. Furthermore, compared to the previous metaheuristic approaches, the suggested methods enabled faster convergence. • Two optimization algorithms are proposed as novel hybrid FORM in structural reliability analysis. • Local adjusting process is proposed in hybrid FORM methods of EPSO and PSO–HS. • PSO–HS and EPSO compared with PSO, HS, IHS, IPSO, LS-PSO and six FORM algorithms. • Proposed methods are more efficient than FORM for high-dimensional problems. [ABSTRACT FROM AUTHOR]