Asian Journal of Civil Engineering

The present study investigates the effectiveness of epoxy injection on the performance of post-heated concrete cylinders confined with carbon fiber-reinforced polymer composites (CFRP). A total of 42 standard dimensions (diameter of 150 mm × 300 mm) were tested under uniaxial compression. These cylinders were divided into two groups regarding the heating, i.e., un-heated and post-heated. Furthermore, the post-heated concrete specimens were further divided into four specimen groups with respect to various temperatures, i.e., 400 °C, 600 °C, 700 °C, and 800 °C, and then cooled to room temperature. The axial compressive behavior of un-heated unconfined (UHUC), un-heated CFRP confined (UHC), post-heated unconfined (PHUC), post-heated CFRP confined (PHC), and epoxy-injected post-heated CFRP confined concrete cylinders (PHEC) were investigated in terms of axial compressive strength (fc′), stiffness (k), energy dissipation capacity (EDC), and restorability. The test results showed that CFRP confinement significantly enhances the fc′ and EDC of the PHC subjected to the mentioned temperatures. Furthermore, the CFRP confinement effectiveness was increased with increasing the level of fire damage (i.e., at a higher temperature). It was found that the fc′ of PHEC at 400 °C was restored up to the design value of UHUC. However, the PHEC was unsuccessful in restoring the design value for temperatures higher than 400 °C.

High-performance concrete (HPC) is designed to be more efficient and shows a higher value of flowability, strength, and durability in comparison to conventional concrete. The strength property is the most critical parameter in concrete structure design it shows a high non-linear correlation with the mixed proportioned ingredients due to its heterogeneous characteristic. Laboratory methods of determining the strength cause loss of resources, time, and materials; hence, numerous attempts to predict the compressive strength of HPC from its combined constituents have been made. The research work focuses on predicting the strength utilizing different machine learning (ML) algorithms such as multi-layer perceptron, support vector regression, and XGBoost with random search and genetic algorithm as a hyperparameter optimization technique. ML algorithms were trained and tested with multination datasets using the cross-validation method. The extreme gradient boosting ensemble algorithm (XGBoost) with genetic algorithm optimization technique showed better accuracy owing to a higher value of R2, and lower values of RMSE, MAE, and MAPE. The genetic XGBoost algorithm performed better in comparison to previously developed models on multination datasets showing better efficacy. A graphical user interface is also developed by the transformation of the ensembled model by means of providing easy to use access.

Structural identification plays an important part in the monitoring of sensitive structures such as onshore wind turbines. Due to the nature of wind, such structures are subjected to turbulent wind fields that cause aerodynamic excitations to the structure. Such turbulent wind fields are hard to be measured, thus output-only identification is required. Output-only identification is a hard category of structural identification methods that is used whenever input excitations are unknown or hard to be measured. With their ability to estimate and reproduce the excitation histories, output-only has established itself as an important method in structural health monitoring. This contribution utilizes a previously developed simplified model to perform an output-only identification to onshore wind turbine towers. The studied wind turbine is an idling 5-MW reference turbine developed by the National Renewable Energy Laboratory (NREL). The effect of the number of sensors distributed on the tower was studied, and obtained results were satisfying in both identification accuracy and force estimation, encouraging to improve the identification scheme by coupling it with a noise filtering method to deal with noisy vibration responses.

This study predicts the shear strength of rectangular hollow reinforced concrete (RC) columns using artificial neural network (ANN). A total of 120 experimental results are collected from literature and used for establishing the machine learning model. The results reveal that the proposed ANN model predicts the shear strength of rectangular hollow RC columns accurately with $${R}^{2}$$ of 0.99. Additionally, the relative importance of input parameters on the calculated shear strength of RC columns is evaluated using Shapley value. Based on the ANN model, a graphical user interface tool is also developed and readily used in predicting the shear strength of rectangular hollow RC columns.

The purpose of this research is to investigate the response of concrete-filled composite tube column under concentric and eccentric loads. To evaluate the influence of PVC pipe on the behavior of concrete-filled composite tubes, concrete-filled PVC pipe (CFPT) were modeled by ABAQUS software. The results demonstrate that the presence of the PVC pipe makes the concrete column more ductile and have a significant effect on the stress–strain curve of the CFPT after the peak strength, in a way that the confinement increased by raising the thickness of PVC pipe, but the confinement effect of the PVC pipe decreased by the increment of the column diameter and compressive strength of the concrete core. The existence of gaps at two ends of the CFPT column does not significantly influence the initial slope of the axial stress-axial strain curve but increases the column’s load capacity and ductility. Peak strength of columns with gaps at two ends is lower than columns without gaps at two ends. Increasing the thickness of the PVC pipe in columns without gaps, with compressive concrete strength of more than 60 MPa, results in an earlier failure. When the eccentric load was applied to this type of column, it did not experience any increase in its initial peak, and columns with gaps at two ends experienced a failure with lower peak strength than the columns without gaps. These types of columns maintain about 50% of their strength under loads with eccentricity up to 20% of column’s diameter.

Grouted sleeve (GS) couplers have been widely used in precast concrete industry in non-seismic regions. However, for seismic applications, previous studies on precast reinforced concrete (RC) columns containing GS couplers showed disrupted plastic hinge formation in the sleeve region and thus limited ductility. Recently, a connection design detail was proposed and it showed significant improvement in ductility using shifted plastic hinge (SPH) mechanism where transition splicing was used (GS connecting steel bars with different sizes) and high-strength steel reinforcement was used in the capacity-protected element (such as footing). This study investigates experimentally the tensile behavior of GS couplers connecting steel bars with transition splicing and different grades intended for use in SPH applications. Nine GS specimens were assembled and tested. The testing parameters were GS coupler size, steel bar diameter, and transition splicing index. Results showed that all GS couplers fully developed the ultimate stress of the steel bars and the failure mode was bar rupture away from the coupler region which is a favorable mode. Also, it was observed that the strain within the coupler region and strain in the normal-strength steel bar (Gr. 420) were related by a ratio which was approximately 1.0 and 0.5 within the linear elastic and nonlinear inelastic zones, respectively, for all different specimen sizes. Furthermore, the results revealed that transition index had no effect on the failure mode, and showed insignificant effect on the stress-strain behavior of the GS couplers. Based on the study results, it is recommended to quantify the strain ratios experimentally for the selected assembly size since these ratios are a crucial part in designing GS precast columns with SPH methodology.

The MR damper parameters used in the vehicle model correspond to a real-world damper and are chosen so that the MR damper model characteristics match those of the experimental damper. The control and response statistics of a non-linear vehicle model utilising an MR damper are produced iteratively using the equivalent linearization method, and the findings are validated using rider optimisation (RO) simulation modelling. The suggested technique is easy to use, robust and well-suited for solving highly non-linear situations. The optimal parameters-arrived performance measure is examined, including ride comfort, tyre force, and protentional power, as well as the optimal design specifications for the front and rear dampers. The optimal control using preview reduces a performance index, which is a collection of vehicle performance parameters such as mass acceleration, displacement and both front and rear suspension stiffness. The results reveal that the developed RO algorithm is capable of determining the optimal parameters of the MR dampers.

This article deals with transient responses in terms of displacement and stresses in composite plates. Eight-node isoparametric elements with five degrees of freedom at each node are used to model the plate. First order shear deformation theory (FSDT) with proper shear correction factor is considered to simulate the strain parameters of the plate. The time history responses of the composite plates with both symmetric and anti-symmetric ply layers against different sinusoidal excitation of different excitation frequencies is computed. The effect of different boundary conditions, ply orientation, plan dimension and plate thickness are studied rigorously. Contour plot for normal stress, inplane shear stress and transverse shear stress is plotted for varying ply orientations and boundary conditions for each ply layer. Comparative studies of various stress contours across different layers in a lamina for similar loading or boundary conditions are also presented. A suggestive guideline for design engineers is also provided in terms of stress contour plot for most suitable ply angle and orientation of a composite plate.

This study presents the performance of three machine learning (ML) models including gradient boosting regression trees (GBRT), artificial neural network model (ANN), and artificial neural network–particle swarm optimization (ANN-PSO) for predicting the axial compression capacity (ACC) of cold‑formed steel elliptical hollow section (EHS) columns. To achieve the goal, a set of 291 data is collected from previous studies to develop GBRT, ANN, and ANN-PSO models. The performance of GBRT, ANN, and ANN-PSO models is evaluated based on the statistical indicators, which are $${R}^{2}, \mathrm{RMSE},\mathrm{ MAPE},$$ and $$i20-\mathrm{index}$$ . The results show that the ANN-PSO model with $${R}^{2}=1.00, \mathrm{RMSE} = 41.3631, \mathrm{MAPE }= 1.3689,$$ and $$i20-\mathrm{index} = 0.9966$$ has the best performance compared to GBRT and ANN models. Moreover, a graphical user interface tool is developed based on the ANN-PSO model for practical designs.