The direct tensile strength of a typical hard rock like granite is measured by a novel apparatus known as compression-to-tensile load transfer (CTLT) device. The rock specimen is prepared in form of a slab containing a central hole and placed in the universal testing machine where the direct tensile stress can be applied to this specimen by implementing a special type of load transferring device which converts the applied compressive load to that of the tensile during the test. In the present work, some typical hard rock specimens of granite are specially prepared and tested in the laboratory to measure their direct tensile strengths. Then, a new load converting device implemented in the universal tensile testing machine is used to cause the rock specimen to be subjected to a direct tensile loading during the test. The compressive load was applied to the transferring device at the rate of 0.02 MPa/s. Numerical modeling of the tested specimens were accomplished using the discrete element method (DEM) and the higher order displacement discontinuity method (HODDM). The tensile failure of granite rock mainly occurs along the horizontal axis. The experimental results were in a good accordance with DEM results and HODDM outputs.

In this study, the structural response of concrete-filled single and double skin steel tubular (CFST and CFDST) composite tapered columns was investigated through the finite element method (FEM). In the development of the FEM model, the concentric axial loading condition and circular section were adopted. Experimental results available in the literature were used to verify the proposed FEM model. In addition, a parametric study was performed to visualize the effectiveness of tapered angle and material strengths on the ultimate capacity of CFST and CFDST tapered columns. To this aim, a total of 60 tapered column samples (including 30 CFST and 30 CFDST columns) were modeled by taking into consideration five tapered angles, two steel tube yield strengths, and three concrete cube compressive strengths. The verification of the FEM model revealed that the developed model has a reliable and trustable assessment capability. It was noticed that the tapered angle was the most crucial parameter, influencing significantly the ultimate axial strength and stiffness of both CFST and CFDST composite tapered columns. As well, it was overtly beheld from the study that CFST composite tapered column specimens had better ultimate axial strength values than CFDST composite tapered column specimens with the same sectional and material properties.

Visual inspection of concrete cracks has been widely used in structural health monitoring (SHM). Capturing high-resolution images is an effective method to visualize a complete crack, but it is difficult to show a whole crack from a single high-resolution image. One feasible method is using image stitching technique to stitch several images into a complete crack map. However, the current image stitching method is a computationally intensive process. Numerous images are required to cover large-scale structures with sufficient resolution, this can be computationally prohibitive. To address this problem, an improved image stitching method for crack damage evaluation is proposed, which can quickly stitch the crack images without affecting the quality of the stitching or the resulting images. Rather than first stitching the images together and then determining the crack maps, we propose to first develop the crack maps for the individual images and then stitch them together. The proposed method reduces the number of redundant matching points between the original images by combining their characteristics during image stitching, so it can reduce the calculation time without affecting the quality. Also, the results will not be influenced by the image stitching seam, which can reduce the complexity of the algorithm. Several experimental results are provided in this article to demonstrate that the proposed method can reduce the calculation time without affecting the quality of image stitching and have better robustness than the current method in use.

To mathematically represent crowd jumping loads, the features of the jumping load of each person, including pulse curve patterns, pulse interval sequences, and pulse energy sequences are considered. These features are essentially high-dimensional random variables. However, they have to be represented in a practically simplified model due to the lack of mathematical tools. The recently emerged generative adversarial networks (GANs) can model high-dimensional random variables well, as demonstrated in image synthesis and text generation. Therefore, this study adopts GANs as a new method for modelling crowd jumping loads. Conditional GANs (CGANs) combined with Wasserstein GANs with gradient penalty (WGANs-GP) are used in pulse curve pattern modelling, where a multi-layer perceptron and convolutional neural network are selected as the discriminator and generator, respectively. For the pulse energy sequence and pulse interval sequence modelling, similar GANs are used, where recurrent neural networks are selected as both the generator and discriminator. Finally, crowd jumping loads can be simulated by connected the pulse samples based on the pulse energy sequence samples and interval sequence samples, generated by the three proposed GANs. The experimental individual and crowd jumping load records are utilized in training GANs to ensure their output can simulate real load records well. Finally, the feasibility of the proposed GANs was verified by comparing the measured structural responses of an existing floor to the predicted structural responses.

Structural Health Monitoring (SHM) is rapidly developing as a multi-disciplinary technology solution for condition assessment and performance evaluation of civil infrastructures. It consists of three parts: data collection, data processing (feature extraction/selection), and decision-making (feature classification). In this research, for effectively reducing a dimension of SHM data, various methods are proposed such as advanced feature extraction, feature subset selection using optimization algorithm, and effective surrogate model based on artificial intelligence methods. These frameworks enhance the capability of the SHM process to tackle with uncertainties and big data problem. To reach such goals, a framework based on three main blocks are proposed here: feature extraction block using wavelet pocket relative energy (WPRE), feature selection block using improved version of binary harmony search algorithm and finally feature classification block using wavelet weighted least square support vector machine (WWLS-SVM). The capability of the proposed framework is compared with various well known methods for each block. Results will be presented using metrics of precision, recall, accuracy and feature-reduction. Furthermore, to show the robustness of the proposed methods, six well-known benchmark datasets of SHM domain are studied. The results validate the suitability of the proposed methods in providing data reduction and accelerating damage detection process.

In the present study, the generation of electrical energy from induced vibrations in a composite beam with piezoelectric layer are studied. Accordingly, using Euler-Bernoulli beam theory and considering two types of air damping (external damping) and structural damping (internal damping), the equations of motion for sandwich beam are obtained and then using the Kantorovich method, the output voltage relations for a composite beam with a piezoelectric layer are extracted. After validating the analytical results with the results in the literature, the effect of various parameters such as external fluid flow rate, fiber angle, and how the piezoelectric layer composite beams are arranged on energy harvesting. Also, the maximum oscillation amplitude are investigated. The results show that by using composite materials and with proper layer design and fiber angle in each layer, a different equivalent modulus of elasticity can be created in the composite beam, which will change the normal frequency of the system and the output voltage range of the circuit. The results show that the angle of the fibers has a significant effect on the damping coefficient of the structure, flexural stiffness, natural frequency and finally energy harvesting. According to the results, it can be seen that the minimum value of voltage per use of fibers with an angle of 50 degrees and the maximum amount of voltage per use of fibers with an angle of zero degrees are occurred.

The compressive strength is an important mechanical feature of concrete that is needed in construction design. Thus, a lot of investigations were carried out to predict the compressive strength of various concretes. However, the prediction models for the compressive strength of cement mortar or paste that include magnetic water (MW) and granulated blast-furnace slag (GBFS) are still limited. The current study has developed hybrid algorithms based on adaptive neuro-fuzzy inference system (ANFIS) for modeling the compressive strength of cement mortar and paste that made with MW and GBFS as a novel mixture content. A total of 144 experimental sets of concrete-compressive strength tests for each cement mortar and paste were collected to train and validate the proposed methods, in which the cycles number of water magnetization, cement, GBFS, superplasticizer contents and curing time are set as the input data while the compressive strength value is set as the output. The developed hybrid algorithms of ANFIS optimized by firefly algorithm (FA), Improved Particle Swarm Optimization (IPSO) and biogeography-based optimization (BBO) algorithms for predicting the compressive strength of the mortar and paste. The proposed models and relevance vector machine (RVM) approach were evaluated and compared. The results showed that the ANFIS-FA outperforms other models for modeling the compressive strength of cement mortar and paste. The adjusted-coefficient of determination and root mean square error values of cement mortar models (96.20%, 92.33%, 92.36% and 89.41%) and (2.17 MPa, 3.10 MPa, 3.18 MPa and 3.06 MPa) and of cement paste models (96.92%, 80.91%, 92.19% and 88.18%) and (2.45 MPa, 5.80 MPa, 4.39 MPa and 5.20 MPa) were determined for ANFIS-FA, ANFIS-IPSO, ANFIS-BBO and RVM models, respectively, which indicate that the ANFIS-FA is a suitable model for estimating the compressive strength of cement mortar and paste that include MW. Moreover, the sensitivity of MW and GBFS is shown high for modeling the compressive strength of cement mortar.

This research is deal with thermal buckling and post-buckling of carbon nanotube/fiber/polymer composite beams. The beam is considered to be under uniform temperature rise. Firstly, the effective material properties of a two phase nanocomposite consisting of CNT and polymer are extracted. Then, the modified Chamis rule is utilized to obtain the equivalent thermo-mechanical properties of multiscale hybrid composite (MHC). Based on the first order shear deformation theory, Von-Karman type of geometrically nonlinear strain-deformation equations and also the virtual work rule, the equilibrium equations of a three phace composite beam are derived. Bifurcation buckling and also the thermal post-buckling is analysed using the generalized differential quadrature technique. In the thermal buckling phenomena, a linear eigenvalue problem is solved; however, due to the nonlinearity, the thermal postbuckling study is performed using an iterative displacement control strategy. After validation study, several novel results demonstrate the influences of length-to-thickness ratio, agglomeration of applied CNTs and fibers in the composite media and number and orientation of layers on the critical temperature and displacement-loading path.

The following article presents the damped forced vibration of layered functionally graded thick beams including material porosities. In case of very thick beams, beam theories fail to satisfy boundary conditions and to predict the mechanical response accurately. So, the two-dimensional (2D) plane continuum model is exploited to model a thick functionally graded layered beam. The beam is composed from three- layers with functionally graded porous materials. The porosity is described by three different distribution models through the layer thickness. Applied forces to the functionally graded beam are assumed to be sinusoidal harmonic point load in time domain. The Kelvin-Voigt viscoelastic constitutive model is used to simulate damping effect. The governing equations are obtained by using Lagrange's equations. In frame of finite element analysis, twelve -node 2D plane element is exploited to discretize the space domain of thick beam. In the solution of the dynamic problem, the Newmark average acceleration method is used. Numerical studies illustrate effects of porosity distribution, stacking sequence, and graduation constant on the dynamic responses of layered functionally graded porous thick beams. The results show that the porosity function, stacking sequences and the damping ratio have a vital role in dynamic response of functionally graded beams. The proposed model can be used in nuclear, marine, and aerospace technologies.

Model predictive control (MPC) is an optimal control algorithm in which the current control action is obtained by solving an optimization problem in the presence of hard and soft constraints in the finite time horizons sequentially. In most cases, neglecting the effects of the external loads in predicting the future responses of the structures lead to inaccurate control action. Therefore, it could be beneficial to consider the effects of external loads in the future within the MPC to improve its accuracy. In this paper, a developed model predictive control (DMPC) scheme is introduced. For this purpose, a forecasting seismic excitation model is formulated by two sequential autoregressive (AR) models. One of those estimates the future output of the seismic excitation and the second one enhances the estimation accuracy. Then, the efficiency of the presented approach is demonstrated by the numerical study of two benchmark buildings equipped with an active tuned mass damper (ATMD). The performance of the proposed MPC is finally compared with the conventional and ideal MPCs. The numerical outputs prove the competency and higher conformity of the proposed MPC with the ideal one almost in all of the cases. Twelve benchmark performance indices are also utilized for determining the superiority of the method. The average conformity values for all of the performance indices for the proposed method in the three- and nine-story buildings are by up to 17.75% and 9% more than the values in conventional one, respectively.

The linear-quadratic regulator (LQR) has been applied to structural vibration control for decades; however, selection of the weighting matrices of an LQR mostly depends on trial and error. In this study, a novel metaheuristic optimization method named as symbiotic organisms search (SOS) algorithm is applied to tuning LQR weighting matrices for active mass damper (AMD) control systems. A 10-story shear building with an active mass damper installed at the top is adopted as a benchmark for numerical simulation in order to realize the optimization performance considering three objective functions for mitigation of structural acceleration. Two common optimization methods including genetic algorithm (GA), and particle swarm optimization (PSO) are also applied to this benchmark for comparison purposes. Numerical simulation results indicate that SOS is superior to GA and PSO on searching the minimized solution of the three objective functions. Meanwhile, minimizing the square root of the sum of the squares of peak modal acceleration achieves the best control performance of structural acceleration among the three objective functions. In addition, force saturation is proposed and applied in the optimization process such that the control force level is close to the force capacity of AMD under specified earthquake intensity. Furthermore, the control performance of the optimized LQR is compared with that of the LQR designed by applying three common weighting selection methods when the 10-story building is subjected to various earthquake excitations. Simulation results demonstrate that the optimized LQR significantly outperforms the three LQRs on structural acceleration responses as expected and reduces story drift slightly better than the three LQRs. Finally, the performance-based optimized LQR is experimentally validated by conducting shake table testing in the laboratory. The experimental results and structural control performance are discussed and summarized thoroughly.

The interest of this work is the analysis of the effect of porosity on the nonlinear thermal stability response of power law functionally graded beam with various boundary conditions. The modelling was done according to the Euler-Bernoulli beam model where the distribution of material properties is imitated polynomial function. The thermal loads are assumed to be not only uniform but linear as well non-linear and the temperature rises through the thickness direction. The effects of the porosity parameter, slenderness ratio and power law index on the thermal buckling of P-FG beam are discussed.