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In damage detection the number of elements is generally more than the number of measured frequencies. Consequently, the corresponding damage detection equation is undetermined and thus has infinite solutions. Since in the damaged structures most of their elements remain healthy, the sparsest solution for the damage detection equation is mostly the actual damage. In the proposed method, the damage equation is first linearized in various ways using random finite difference increments. The sparsest solutions for created linear system of equations are derived using basis pursuit. These solutions are considered as the first population for a continuous genetic algorithm to obtain the damage solution. For investigation of the proposed method three case studies are considered. Simulation results confirm the efficiency of the proposed method compared to those found in the literature.

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A novel two-stage algorithm for detection of damages in large-scale structures under static loads is presented. The technique utilizes the vector of response change (VRC) and sensitivities of responses with respect to the elemental damage parameters (RSEs). It is shown that VRC approximately lies in the subspace spanned by RSEs corresponding to the damaged elements. The property is leveraged in the first stage of the proposed method by seeking RSEs whose spanned subspace best contains the VRC. Consequently, the corresponding elements are regarded as damage candidates. To alleviate the exploration among RSEs, they are first partitioned into several clusters. Subsequently, discrete ant colony optimization (ACO) is utilized to find the clusters containing the RSEs of damaged elements. In the second stage of the algorithm, damage amounts for the restricted elements are determined using a continuous version of ACO. Two numerical examples are studied. The results illustrate that the method is both robust and efficient for detection of damages in large-scale structures.

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This study focuses on the topology optimization of structures using a hybrid of level set method (LSM) incorporating sensitivity analysis and isogeometric analysis (IGA). First, the topology optimization problem is formulated using the LSM based on the shape gradient. The shape gradient easily handles boundary propagation with topological changes. In the LSM, the topological gradient method as sensitivity analysis is also utilized to precisely design new holes in the interior domain. The hybrid of these gradients can yield an efficient algorithm which has more flexibility in changing topology of structure and escape from local optimal in the optimization process. Finally, instead of the conventional finite element method (FEM) a Non–Uniform Rational B–Splines (NURBS)–based IGA is applied to describe the field variables as the geometry of the domain. In IGA approach, control points play the same role with nodes in FEM, and B–Spline functions are utilized as shape functions of FEM for analysis of structure. To demonstrate the performance of the proposed method, three benchmark examples widely used in topology optimization are presented. Numerical results show that the proposed method outperform other LSMs.

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Shear wave velocity (V_s) is known as one of the fundamental material parameters which is useful in dynamic analysis. It is especially used to determine the dynamic shear modulus of the soil layers. Nowadays, several empirical equations have been presented to estimate the shear wave velocity based on the results from Standard Penetration Test (SPT) and soil type. Most of these equations result in different estimation of V_s for the same soils. In some cases a divergence of up to 100% has been reported. In the following study, having used the field study results of Urmia City and Artificial Neural Networks, a new correlation between V_s and several simple geotechnical parameters (i.e. Modified SPT value number (N₆₀), Effective overburden stress, percentage of passing from Sieve #200 (Fc), plastic modulus (PI) and mean grain size (d₅₀)) is presented. Using sensitivity analysis it is been shown that the effect of PI in V_s prediction is more than that of N₆₀ in over consolidated clays. It is also observed that Fc has a high influence on evaluation of shear wave velocity of silty soils.

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This research focuses on the effects of stiffeners and architectural opening on the steel shear wall topology optimization. To this aim, four relevant issues have been considered. The first issue is the optimality Pattern of the shear wall without stiffeners. The second is the Optimality Pattern of the shear wall with stiffeners in two directions. The third is the investigation on the topology optimization of the shear walls with fixed opening and the fourth is the multi-material topology optimization of the above issues. In the optimize process, the level set method based on the shape sensitivity and the finite element analysis for two-dimensional linear elastic problems has been used. The level set function implicitly indicated the boundaries of the design domain. Several numerical examples are used to demonstrate the optimal paths in the steel shear walls. The results show that optimal values have been changed by replacing stiffeners and creating openings in the wall, but the optimal topologies almost have a shape like a concentric bracing. Also, in the conventional shear walls (one material) the horizontal stiffeners have a significant effect on their performance.

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This paper proposes a method for structural damage detection through the sensitivity analysis of modal shapes in the calculation of modal strain energy (MSE). For this purpose, sensitivity equations were solved to determine the strain energy based on dynamic data (i.e., modal shapes). An objective function was then presented through the sensitivity-based MSE to detect structural damage. Due to the nonlinearity of sensitivity equations, the objective function of the proposed formulation can be minimized through the shuffled shepherd optimization algorithm (SSOA). The first few modes were employed for damage detection in solving the inverse problem. The proposed formulation was evaluated in a few numerical examples under different conditions. The numerical results indicated that the proposed formulation was efficient and effective in solving the inverse problem of damage detection. The proposed method not only minimized sensitivity to measurement errors but also effectively identified the location and severity of structural damage.
 

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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.