This paper presents the vibration analysis of cracked ceramic-reinforced aluminum composite beams by using a method based on changes in modal strain energy. The crack is considered to be straight. The effective properties of composite materials of the beams are estimated through Mori-Tanaka micromechanical model. Comparison study and numerical simulations with various parameters; ceramic volume fraction, reinforcement aspect ratio, ratio of the reinforcement Young's modulus to the matrix Young's modulus and ratio of the reinforcement density to the matrix density are taken into investigation. Results demonstrate the pronounced effects of these parameters on intact and cracked ceramic aluminum beams.

A new hybrid quasi-3D and 2D high-order shear deformation theory is studied in this mathematical formulation, for an investigation of the bending, free vibrations and buckling influences on a functionally graded material plate. The theoretical formulation has been begun by a displacement field of five unknowns, governing the transverse displacement across the thickness of the plate by bending, shearing and stretching. The transverse shear deformation effect has been taken into consideration, satisfying the stress-free boundary conditions, especially on plate free surfaces as parabolic variation through its thickness. Thus, the mechanical properties of the functionally graded plate vary across the plate thickness, following three distributions forms: the power law, exponential form and the Mori-Tanaka scheme. The mechanical properties are used to develop the equations of motion, obtained from the Hamilton principle, and solved by applying the Navier-type solution for simply supported boundary conditions. The results obtained are compared with other solutions of 2D, 3D and quasi-3D plate theories have been found in the literature.

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, large curvature information contained in the images is extracted for the crack identification based on an improved curvature filtering method. Secondly, a graph-based model is developed for the image sub-blocks anomalies calculation where the baseline of the sub-blocks is acquired by crack-free samples. Once the anomaly is large than the acquired baseline, the sub-block is considered as crack-contained block. The experimental results indicate that the proposed method performs better than convolutional neural network method even under different curvature structures and illumination conditions. This work therefore provides a useful tool for concrete slabs crack detection and is broadly applicable to variety of infrastructure systems.

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

Steel anchor bolts are installed in concrete using a variety of methods. One of the most common methods of anchor bolt installation is using epoxy resin as an infill material injected into the drilled hole to act as a bonding material between the steel bolt and the surrounding concrete. Typical design standards assume uniform stress distribution along the length of the anchor bolt accompanied with single crack leading to pull-out failure. Experimental evidence has shown that the steel anchor bolts fail owing to the multiple failure patterns, hence these design assumptions are not realistic. In this regard, the presented research work details the analytical model that takes into consideration multiple micro cracks in the infill material induced via impact loading. The impact loading from the Schmidt hammer is used to evaluate the bond condition bond condition of anchor bolt and the epoxy material. The added advantage of the presented analytical model is that it is able to take into account the various type of end conditions of the anchor bolts such as bent or U-shaped anchors. Through sensitivity analysis the optimum stiffness and shear strength properties of the epoxy infill material is achieved, which have shown to achieve lower displacement coupled with reduced damage to the surrounding concrete. The accuracy of the presented model is confirmed by comparing the simulated deformational responses with the experimental evidence. From the comparison it was found that the model was successful in simulating the experimental results. The proposed model can be adopted by professionals interested in predicting and controlling the deformational response of anchor bolts.

A high-frequency vibration control method is proposed in this paper for Pumped Storage Power Plants (PSPPs) using Eddy Current Tuned Mass Damper (EC-TMD), based on which a new type of EC-TMD device is designed. The eddy current damper parameters are optimized by numerical simulation. On this basis, physical simulation model tests are conducted to compare and study the effect of structural performance with and without damping, different control strategies, and different arrangement positions of TMD. The test results show that EC-TMD can effectively reduce the control effect under high-frequency vibration of the plant structure, and after the additional damping device forms EC-TMD, the energy dissipation is further realized due to the intervention of eddy current damping, and the control effect is subsequently improved. The Multi-Tuned Mass Damper (MTMD) control strategy broadens the tuning band to improve the robustness of the system, and the vibration advantage is more obvious. Also, some suggestions are made for the placement of the dampers to promote their application.

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

In near-fault earthquake prone areas, the velocity pulse-like seismic waves often results in excessive horizontal displacement for structures, which may result in severe structural failure during large or near-fault earthquakes. The recently developed isolator-gap damper (IGD) systems provide a solution for the large horizontal displacement of long period base-isolated structures. However, the hysteresis characteristics of the IGD system are significantly different from the traditional hysteretic behavior. At present, the hysteretic behavior is difficult to be reflected in the structural analysis and performance evaluation especially under random earthquake excitations for lacking of effective analysis models which prevent the application of this kind of IGD system. In this paper, we propose a mathematical hysteretic model for the IGD system that presents its nonlinear hysteretic characteristics. The equivalent linearization is conducted on this nonlinear model, which requires the variances of the IGD responses. The covariance matrix for the responses of the structure and the IGD system is obtained for random earthquake excitations represented by the Kanai-Tajimi spectrum by solving the Lyapunov equation. The responses obtained by the equivalent linearization are verified in comparison with the nonlinear responses by the Monte Carlo simulation (MCS) analysis for random earthquake excitations.

This work proposes a novel contactless vibration damping and thermal isolation tripod platform based on Superconducting Magnetic Levitation (SML). This prototype is suitable for cryogenic environments, where classical passive, semi active and active vibration isolation techniques may present tribological problems due to the low temperatures and/or cannot guarantee an enough thermal isolation. The levitating platform consists of a Superconducting Magnetic Levitation (SML) with inherent passive static stabilization. In addition, the use of Operational Modal Analysis (OMA) technique is proposed to characterize the transmissibility function from the baseplate to the platform. The OMA is based on the Stochastic Subspace Identification (SSI) by using the Expectation Maximization (EM) algorithm. This paper contributes to the use of SSI-EM for SML applications by proposing a step-by-step experimental methodology to process the measured data, which are obtained with different unknown excitations: ambient excitation and impulse excitation. Thus, the performance of SSI-EM for SML applications can be improved, providing a good estimation of the natural frequency and damping ratio without any controlled excitation, which is the main obstacle to use an experimental modal analysis in cryogenic environments. The dynamic response of the 510 g levitating platform has been characterized by means of OMA in a cryogenic, 77 K, and high vacuum, 1E-5 mbar, environment. The measured vertical and radial stiffness are 9872.4 N/m and 21329 N/m, respectively, whilst the measured vertical and radial damping values are 0.5278 Nm/s and 0.8938 Nm/s. The first natural frequency in vertical direction has been identified to be 27.39 Hz, whilst a value of 40.26 Hz was identified for the radial direction. The determined damping values for both modes are 0.46% and 0.53%, respectively.

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.