Turkey is a transcontinental country located partly in Asia and partly in Europe, and hosted by diverse civilizations including Hittite, Urartu, Lydia, Phrygia, Pontius, Byzantine, Seljuk's and Ottomans. At various times, these built many historic monuments representing the most significant characteristics of their civilizations. Today, these monuments contribute enormously to the esthetic beauty of environment and important to many cities of Turkey in attracting tourism. The survival of these monuments depends on the investigation of structural behavior and implementation of needed repairing and/or strengthening applications. Hence, many countries have made deeper investigations and regulations to assess their monuments' structural behavior. This paper presents the dynamic behavior investigation of a monumental masonry mosque, the "İskenderpaşa Mosque" in Trabzon (Turkey), by performing an experimental examination with non-destructive testing. The dynamic behavior investigation was carried out by determining the dynamic characteristic called as natural frequencies, mode shapes and damping ratios. The experimental dynamic characteristics were extracted by Operational Modal Analysis (OMA). In addition, Finite Element (FE) model of masonry mosque was constructed in ANSYS software and the numerical dynamic characteristics such as natural frequencies and mode shapes were also obtained and compared to experimental ones. The paper aims at presenting the non-destructive testing procedure of a masonry mosque as well as the comparison of experimental and numerical dynamic characteristics obtained from the mosque.

The hinge joint is the key to the overall cooperative working performance of the assembled beam bridge, and it is also the weakest part during the service period. This paper proposes a method for monitoring and evaluating the lateral cooperative working performance of fabricated beam bridges based on dynamic strain correlation coefficient indicator. This method is suitable for monitoring and evaluation of hinge joints status between prefabricated girders and overall cooperative working performance of bridge, without interruption of traffic and easy implementation. The remote cloud monitoring and diagnosis system was designed and implemented on a real assembled beam bridge. The algorithms of data preprocessing, online indicator extraction and status diagnosis were given, and the corresponding software platform and scientific computing environment for cloud operation were developed. Through the analysis of real bridge monitoring data, the effectiveness and accuracy of the method are proved and it can be used in the health monitoring system of such bridges.

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

This paper proposes a novel high performance vibration control device, multiple tuned mass dampers-inerters (MTMDI), to suppress the oscillatory motions of structures. The MTMDI, similar to the MTMD, involves multiple tuned mass damper-inerter (TMDI) units. In order to reveal the basic performance of the MTMDI, it is installed on a single degree-of-freedom (SDOF) structure excited by the ground acceleration, and the dynamic magnification factors (DMF) of the structure-MTMDI system are formulated. The optimization criterion is determined as the minimization of maximum values of the relative displacement's DMF for the controlled structure. Based on the particle swarm optimization (PSO) algorithm to tune the optimum parameters of the MTMDI, its performance has been investigated and evaluated in terms of control effectiveness, strokes, stiffness and damping coefficient, inerter element force, and robustness in frequency domain. Meanwhile, further comparison between the MTMDI with MTMD has been conducted. Numerical results clearly demonstrate the MTMDI outperforms the MTMD in control effectiveness and strokes of mass blocks. Additionally, in the aspects of frequency perturbations on both earthquake excitations and structures, the robustness of the MTMDI is also better than the MTMD.

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

By employing a quasi-3D plate formulation, the present research studies static stability of magneto-electro-thermo-elastic functional grading (METE-FG) nano-sized plates. Accordingly, influences of shear deformations as well as thickness stretching have been incorporated. The gradation of piezo-magnetic and elastic properties of the nano-sized plate have been described based on power-law functions. The size-dependent formulation for the nano-sized plate is provided in the context of nonlocal elasticity theory. The governing equations are established with the usage of Hamilton's rule and then analytically solved for diverse magnetic-electric intensities. Obtained findings demonstrate that buckling behavior of considered nanoplate relies on the variation of material exponent, electro-magnetic field, nonlocal coefficient and boundary conditions.

Shape memory alloy (SMA)-based Superelasticity-assisted Slider (SSS) is proposed as an engineering solution to practically exploit the well-accepted advantages of both sliding isolation and SMA-based recentering. Self-centering capability in SSS is provided by austenitic SMA cables (or wire ropes), recently attracting a lot of interest and attention in earthquake engineering and seismic isolation. The cables are arranged in various novel and conventional configurations to make SSS versatile for aseismic design and retrofit of structures. All the configurations are detailed with thorough technical drawings. It is shown that SSS is applicable without the need for Isolation Units (IUs). IUs, at the same time, are devised for industrialized applications. The proof-of-concept study is carried out through the examination of mechanical behavior in all the alternative configurations. Force-displacement relations are determined. Isolation capabilities are predicted based on the decreases in seismic demands, estimated by the increases in effective periods and equivalent damping ratios. Restoring forces normalized relative to resisting forces are assessed as the criteria for self-centering capabilities. Lengths of SMA cables required in each configuration are calculated to assess the cost and practicality. Practical implementation is realized by setting up a small-scale IU. The effectiveness of SSS under seismic actions is evaluated using an innovative computer model and compared to those of well-known Isolation Systems (ISs) protecting a reference building. Comparisons show that SSS seems to be an effective IS and suitable for earthquake protection of both structural and non-structural elements. Further research aimed at additional validation of the system are outlined.

The cold-formed steel storage racks are extensively employed in various industries applications such as storing products in reliable places and storehouses before distribution to the market. Racking systems lose their stability under lateral loads, such as seismic actions due to the slenderness of elements and low ductility. This justifies a need for more investigation on methods to improve their behavior and increase their capacity to survive medium to severe loads. A standardized connection could be obtained through investigation on the moment resistance, value of original rotational stiffness, ductility, and failure mode of the connection. A total of six monotonic tests were carried out to determine the behavior of the connection of straight 2.0 mm, and 2.6 mm thickness connects to 5 lug end connectors. Then, the obtained results are benched mark as the original data. Furthermore, an extreme learning machine (ELM) technique has been employed to verify and predict both moment and rotation results. Out of 4 connections, increase the ultimate moment resistance of connection by 13% and 18% for 2.0 mm and 2.6 mm upright connection, respectively.

Over recent years, the interest for vegetables and fruits in all seasons and places has much increased, from where diverse countries have directed to the commercial production in greenhouse. In this article, we propose an algorithm based on wireless sensor network technologies that monitor the microclimate inside a greenhouse and linear equations model for optimization plant production and material cost. Moreover, we also suggest a novel design of an intelligent greenhouse. We validate our algorithms with simulations on a benchmark based on experimental data made at lNRA of Montfavet in France. Finally, we calculate the statistical estimators RMSE, TSSE, MAPE, EF and R². The results obtained are promising, which shows the efficiency of our proposed system.