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Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.

Firefly algorithm is a nature-inspired metaheuristic algorithm inspired by the flashing behavior of fireflies. It is originally proposed for continuous problems. However, due to its effectiveness and success in solving continuous problems, different studies are conducted in modifying the algorithm to suit discrete problems. Many engineering as well as optimization problems from other disciplines involve discrete variables. Recent reviews on the application and modifications of firefly algorithm mainly focus on continuous problems. This paper is devoted to the detailed review of the modifications done on firefly algorithm in order to solve optimization problems with discrete variables. Hence, advances on the application of firefly algorithm for optimization problems with binary, integer as well as mixed variables will be discussed. Possible future works will also be highlighted.

The aim of this study is to illustrate and compares the use of Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) for multi-objective optimization of two and three dimensional moment resisting steel structures subjected to earthquake loads. For this purpose, steel buildings with different characteristics are designed under earthquakes using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and PSO as a tool to achieve the best structure in terms of: minimize the total structural weight (which is directly related with the costs), control of the maximum inter-story drift, and to satisfy the strength requirements of the AISC-LRFD specification. It is considered that all the steel structures are constituted by elements with W section (256 in total) taken from the LRFD-AISC Database. Although, the GAs and PSO are applied for moment resisting steel structures, the concepts can be extended for other structural systems. It is concluded that the use of NSGA-II and PSO reduce the structural weight and they are a very useful tools to improve the structural performance of the buildings. Finally, the structural buildings obtained via PSO are in general better solutions in comparison with the NSGA-II approach.

The construction industry is nonhomogeneous and also managing construction projects are more difficult in today’s world. Construction projects are huge and contractors want to accomplish them within a short time in this fast changing era. Therefore, the time and resource have to be managed for a successful construction project management. Resource leveling is one of the primary tools used for managing resources. The target is leveling the resources within a minimum time period to complete the project successfully. Resource constrained project scheduling problems (RCPSP) are a Non-deterministic Polynomial-time hard (NP-hard) problem therefore heuristic methods can be used to solve it. This paper presents a harmony search method for solving the RCPSP. In order to compare the performance of the developed software three examples were chosen from the literature. Computational results indicate that the harmony search method is more effective, rapid and suitable for the RCPSP than existing solutions.

The influence of different proportions of clay minerals on the moisture density relationship and index properties of clay soils are not clearly understood because of the different factors that come into play in natural soil formation. This study used 11 different remolded samples prepared by combining double mixtures of commercially available powdered kaolinite, montmorillonite and quartz in proportions of 30%, 50%, 70% and 100% based on dry unit weight to investigate the relationship between different proportions of clay minerals with index properties and compaction characteristics of clay soils. The results described the influence of different proportions of clay minerals on the liquid limit, plastic limit and plasticity index measured using the fall cone penetration test. Also, the variation of maximum dry density and optimum moisture content with different clay mineral proportions has been analyzed. In addition, equations are presented with significant correlations to show the relationship between Atterberg limits, compaction parameters and the proportion of clay minerals. Knowledge on the effect of the different clay minerals on the moisture density relationship and index properties can be used to predict the behavior of compacted clay soils encountered in engineering practices and reduce the time and effort needed for assessing the suitability of clay soils and clay liners for engineering use.

A coordinated traffic control method on urban expressways is proposed, which combines ramp control and Variable Speed Limits (VSL). A modified Particle Swarm Optimization (PSO) algorithm is presented to implement the coordinated control. We employ a model predictive control schema to offer online control on expressways. With spatial and temporal constraints on speed limits values during optimization, the modified PSO algorithm aims to search the optimal control signals for the coordinated control method. Instead of continuous VSL values, discrete VSL signals are used, which is more applicable for practical expressway control. The objective function in this paper considers a weighted summation of total time spent and total travel distance, which balances the expressway traffic density and the traffic outflow. Simulation results verifies that the proposed approach has its applicability and a better control performance.

A river system includes the combination of flows occurring simultaneously in the main river and its contributing tributaries. Any change in the flow condition of the river system is caused due to changes in flow of the main river and/or contributing tributaries. An accurate flow forecasting at multiple sections of a river system is worthy for issuing early warning to the imminent floods and in regulating the reservoir outflows. The application of multiple-inputs and multiple-outputs (MIMO) model is an effective way for simultaneous flow forecasting as it provides interrelation among multiple input and multiple output variables simultaneously. In the present study an Artificial Neural Networks (ANN) based MIMO model has been developed for Barak river system in Assam, India using Partially Recurrent Neural Network (PRNN) and Nonlinear Autoregressive with Exogenous Inputs (NARX) approaches. Performance of the model using both NARX and PRNN provide an efficacy with coefficient of efficiency (CE) > 0.87 and Mean Absolute Percentage Error (MAPE) < 7.66% at 12 hour lead time forecasting. This indicates satisfactory model performances for simultaneous flow forecasting at multiple sections of a river system however the results obtained by MIMO using NARX (MIMONARX) perform better than MIMO using PRNN (MIMO-PRNN) in terms of statistical performance criterion.

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications. Structural optimization approaches seek to determine the optimal design, by considering material performance, cost, and structural safety. The design approaches aim to reduce the built environment’s energy use and carbon emissions. This comprehensive review examines optimization techniques, including size, shape, topology, and multi-objective approaches, by integrating these methodologies. The trends and advancements that contribute to developing more efficient, cost-effective, and reliable structural designs were identified. The review also discusses emerging technologies, such as machine learning applications with different optimization techniques. Optimization of truss, frame, tensegrity, reinforced concrete, origami, pantographic, and adaptive structures are covered and discussed. Optimization techniques are explained, including metaheuristics, genetic algorithm, particle swarm, ant-colony, harmony search algorithm, and their applications with mentioned structure types. Linear and non-linear structures, including geometric and material nonlinearity, are distinguished. The role of optimization in active structures, structural design, seismic design, form-finding, and structural control is taken into account, and the most recent techniques and advancements are mentioned.

This paper presents an art-inspired optimization algorithm, which is called Stochastic Paint Optimizer (SPO). The SPO is a population-based optimizer inspired by the art of painting and the beauty of colors plays the main role in this algorithm. The SPO, as an optimization algorithm, simulates the search space as a painting canvas and applies a different color combination for finding the best color. Four simple color combination rules without the need for any internal parameter provide a good exploration and exploitation for the SPO. The performance of the algorithm is evaluated by twenty-three mathematical well-known benchmark functions, and the results are verified by a comparative study with recent well-studied algorithms. In addition, a set of IEEE Congress of Evolutionary Computation benchmark test functions (CEC-C06 2019) are utilized. On the other hand, the Wilcoxon test, as a non-parametric statistical test, is used to determine the significance of the results. Finally, to prove the practicability of the SPO, this algorithm is applied to four different structural design problems, known as challenging problems in civil engineering. The results of all these problems indicate that the SPO algorithm is able to provide very competitive results compared to the other algorithms.

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R2) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R2 of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.