In order to investigate the seismic performance of regionally confined concrete circular column with trapezoid stirrups (TRCCC) under high axial compression ratio, the confinement mechanism of regionally confined concrete was analyzed. Three regionally confined concrete circular columns with trapezoid stirrups were designed, and low cyclic loading tests were conducted at three different axial compression ratios (0.9, 1.1, 1.25) to study the failure mode, hysteresis curve, skeleton curve, deformation capacity, stiffness degradation and energy dissipation capacity of the specimens. The results indicate that the form of regional confinement concrete provides more uniform confinement to the normal confinement, and the confinement efficiency at the edges is 1.4 times that of normal confined concrete. The ductility coefficients of the specimens were all greater than 3 under high axial compression ratios, and the stiffness and horizontal bearing capacity increased with the increase of axial compression ratio. Therefore, it is recommended that the code of design specifications can appropriately relax the axial compression ratio limit for TRCCC. Finally, the spacing between stirrups of TRCCC was analyzed using ABAQUS software. The results showed that as the spacing between the stirrups decreased, the cracking load and peak load of TRCCC increased continuously, but the rate of increase decreases.

This work aims to explore the static behaviour of a tapered functionally graded porous plate (FGPP) with even and uneven porosity distributions resting on two parametric elastic foundations. The plate under investigation is subjected to bi-sinusoidal loading and the edges of the plate are exposed to different combinations of edge restrictions. In order to examin the static behaviour, bending factors (BF) related to bending and normal stresses have been evaluated using classical plate theory. To achieve this, the governing equations have been derived employing the energy concept. And to solve it, the Rayleigh-Ritz method with an algebraic function has been utilised; it is simple, precise, and computationally intensive. After convergence and validation analyses, new findings are made available. The BF of the plate have been exhaustively examined to explain the influence of aspect ratios, material property index, porosity factor, taper factor, and Winkler and Pasternak stiffness. It is observed that the BF of an elastically supported FGPP are influenced by the index of material propery and the aspect ratio. Findings also indicate that the impact of porosity is more when it is spread evenly, as opposed to when it is unevenly distributed. Further, the deformed plate's structure is significantly influenced by the different thickness variations. Examination of bending characteristics of FGPP having different new cases of thickness variations with different types of porosity distribution under fifteen different mixed edge constraints is the prime novality of this work. Results presented are reliable enough to be taken into account for future studies.

In the construction industry, composite structures have revolutionized traditional design principles, opening innovative possibilities. The Concrete Encased - Concrete Filled Steel Tubular (CE-CFST) column stands out as a distinctive composite structure, offering structural stability and resilience for various engineering applications. Comprising Reinforced Concrete (RC) and Concrete Filled Steel Tubular (CFST) components, CE-CFST columns are valued for their inherent properties, including ductility and rigidity, CE-CFST is commonly used in the construction of bridges, high-rise buildings, and more. This article aims to provide a concise overview of the evolutionary development of CE-CFST columns and their performance in structural applications. Through a comprehensive review, the study delves into the behaviour of CE-CFST columns under different scenarios. It examines the influences of key parameters such as size, infills, cross section, failure causes, and design codes on the performance of CE-CFST columns, highlighting their enhanced functionality and future potential. Moreover, the review meticulously examines previous applications of CE-CFST columns, offering insights into their practical implementation.

Given the increased interest in enhancing structural sustainability, the current study sought to apply multiobjective optimization to a footbridge with a steel-concrete composite I-girder structure. It was considered as objectives minimizing the cost for building the structure, the environmental impact assessed by CO2 emissions, and the vertical accelerations created by human-induced vibrations, with the goal of ensuring pedestrian comfort. Spans ranging from 15 to 25 meters were investigated. The resistance of the slab's concrete, the thickness of the slab, the dimensions of the welded steel I-profile, and the composite beam interaction degree were all evaluated as design variables. The optimization problem was handled using the Multiobjective Harmony Search (MOHS) metaheuristic algorithm. The optimization results were used to generate a Pareto front for each span, allowing us to assess the correlations between different objectives. By evaluating the values of design variables in relation to different levels of pedestrian comfort, it was identified optimal values that can be employed as a starting point in predimensioning of the type of structure analyzed. Based on the findings analysis, it is possible to highlight the relationship between the structure's cost and CO2 emission objectives, indicating that cost-effective solutions are also environmentally efficient. Pedestrian comfort improvement is especially feasible in smaller spans and from a medium to a maximum level of comfort, but it becomes expensive for larger spans or for increasing comfort from minimum to medium level.

The utilization of geopolymer recycled aggregate concrete (GRAC) as the infilled core of the concrete-filled steel tubular (CFST) columns provides superior economic and environmental benefits. However, limited research exists within the field of geopolymer recycled aggregate concrete considered a green and sustainable material, in addition to the limitation of the design guidelines to predict the behavior of such an innovative new material combination. Moreover, the behavior of high-strength concrete is different from the normal-strength one, especially when there is another material of high-strength properties, such as the steel tube. This paper aims to investigate the behavior of the axially loaded square high-strength GRACFST columns through the nonlinear finite element analysis (NLFEA). A total of thirty-two specimens were simulated using ABAQUS/Standard software with three main variables: recycled aggregate replacement ratio (0, 30, and 50) %, width-to-thickness ratios (52.0, 32.0, 23.4, and 18.7), and length-to-width ratio (3, 5, 9, and 12). During the analysis, the response in terms of the axial load versus the longitudinal strain was recorded and plotted. In addition, various mechanical properties were calculated and analyzed. In view of the results, it has been demonstrated that the mechanical properties of high-strength GRACFST columns such as ultimate load-bearing capacity, compressive stiffness, energy absorption capacity, and ductility increase with the increase of the steel tube thickness owing to the improvement of the confinement effect of the steel tube. In contrast, the incorporation of the recycled aggregate adversely affected the mentioned properties except the ductility, while the increase of the recycled aggregate replacement ratio improved the column's ductility. Moreover, it has been found that the increase in the length-to-width ratio significantly reduced both the failure strain and the energy absorption capacity. Finally, the obtained NLFEA results of the ultimate load-bearing capacity were compared with the corresponding predicted capacities by numerous codes. It has been concluded that AISC, ACI, and EC give conservative predictions for the ultimate load-bearing capacity since the confinement effect was not considered by these codes.

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

In this paper, the dynamic flexural stiffness of concrete-filled steel tubular (CFST) members is investigated based on vibration modal testing and a Bayesian model updating procedure. To reflect the actual service states of CFST members, a 3-stage modal testing procedure is developed for 6 circular CFST beam-columns, in which the modal parameters of the specimens under varying axial load levels are extracted. In the model updating procedure, a Timoshenko beam element model is first established, in which the influence of shear deformation and rotational inertia are incorporated. Subsequently, a 2-round Bayesian model updating strategy is proposed to calculate the dynamic flexural stiffness of the specimens, which could effectively consider the influence of physical constraints in the updating process and achieve reasonably well results. Analysis of the updating results shows that with the increase of the axial load level, degradation of the flexural stiffness is significantly influenced by the load eccentricity. It shows that the cracking of the core concrete is the primary reason for the flexural stiffness degradation of CFST beam-columns. Finally, based on comparison with equations proposed by several design standards, the calculation methods for the dynamic flexural stiffness of CFST members is recommended.

Damage detection in structures using the change of modal parameters (modal shapes and natural frequencies) has achieved satisfactory results. However, as modal shapes and natural frequencies alone may not provide enough information to accurately detect damages. Therefore, a hybrid singular value decomposition and deep belief network approach is developed to effectively identify damages in aluminum plate structures. Firstly, damage locations are determined using singular value decomposition (SVD) to reveal the singularities of measured displacement modal shapes. Secondly, using experimental modal analysis (EMA) to measure the natural frequencies of damaged aluminum plates as inputs, deep belief network (DBN) is employed to search damage severities from the damage evaluation database, which are calculated using finite element method (FEM). Both simulations and experimental investigations are performed to evaluate the performance of the presented hybrid method. Several damage cases in a simply supported aluminum plate show that the presented method is effective to identify multiple damages in aluminum plates with reasonable precision.