This paper focuses on the design of an intelligent control system. The used techniques are based on Neuro Fuzzy approaches applied to a magnetorheological damper in order to reduce the vibrations over footbridges; it has been applied to the Science Museum Footbridge of Valladolid, particularly. A model of the footbridge and of the damper has been built using different simulation tools, and a successful comparison with the real footbridge and the real damper has been carried out. This simulated model has allowed the reproduction of the behaviour of the footbridge and damper when a pedestrian walks across the footbridge. Once it is determined that the simulation results are similar to real data, the control system is introduced into the model. In this sense, different strategies based on Neuro Fuzzy systems have been studied. In fact, an ANFIS (Artificial Neuro Fuzzy Inference System) method has also been used, in addition to an alternative Neuro Fuzzy approach. Several trials have been carried out, using both techniques, obtaining satisfactory results after using these techniques.

The goal of this study is to present numerical modeling and analytical testing in order to evaluate an innovative space truss typed temporary structure, which is used to maintenance and repair of road tunnels. The present space truss structure has merits to use UL-700 high strength steel tube as members and to carry out maintenance and repair works of road tunnels without blocking cars and transportations. Numerical modeling and analytical testing of the space truss are investigated by using commercial engineering software, i.e., ABAQUS 6.5-1, and then it is verified that the truss structure has both structural safety and effective function for maintenances and repairs of road tunnels.

The effectiveness of the various control algorithms for semi-active structural control systems proposed in the literature is highly questionable when dealing with earthquake actions, which never reach a steady state. From this perspective, the paper summarizes the results of an experimental activity aimed to compare the effectiveness of four different semi-active control algorithms on a structural mock up representative of a class of structural systems particularly prone to seismic actions. The controlled structure is a near full scale 2-story steel frame, equipped with two semi-active bracing systems including two magnetorheological dampers designed and manufactured in Europe. A set of earthquake records has been applied at the base of the structure, by utilizing a shaking table facility. Experimental results are compared in terms of displacements, absolute accelerations and energy dissipation capability. A further analysis on the percentage incidence of undesired and/or unpredictable operations corresponding to each algorithm gives an insight on some factors affecting the reliability and, in turn, the real effectiveness of semi-active structural control systems.

Structural health monitoring along with damage detection and assessment of its severity level in non-accessible reinforced concrete members using piezoelectric materials becomes essential since engineers often face the problem of detecting hidden damage. In this study, the potential of the detection of flexural damage state in the lower part of the mid-span area of a simply supported reinforced concrete beam using piezoelectric sensors is analytically investigated. Two common severity levels of flexural damage are examined: (i) cracking of concrete that extends from the external lower fiber of concrete up to the steel reinforcement and (ii) yielding of reinforcing bars that occurs for higher levels of bending moment and after the flexural cracking. The purpose of this investigation is to apply finite element modeling using admittance based signature data to analyze its accuracy and to check the potential use of this technique to monitor structural damage in real-time. It has been indicated that damage detection capability greatly depends on the frequency selection rather than on the level of the harmonic excitation loading. This way, the excitation loading sequence can have a level low enough that the technique may be considered as applicable and effective for real structures. Further, it is concluded that the closest applied piezoelectric sensor to the flexural damage demonstrates higher overall sensitivity to structural damage in the entire frequency band for both damage states with respect to the other used sensors. However, the observed sensitivity of the other sensors becomes comparatively high in the peak values of the root mean square deviation index.

Shanghai Tower is a composite structure building with a height of 632 m. In order to verify the structural properties and behaviors in construction and operation, a structural health monitoring project was conducted by Tongji University. The monitoring system includes sensor system, data acquisition system and a monitoring software system. Focusing on the health monitoring in construction, this paper introduced the monitoring parameters in construction, the data acquisition strategy and an integration structural health monitoring (SHM) software. The integration software - Structural Monitoring/ Analysis/ Evaluation System (SMAE) is designed based on integration and modular design idea, which includes on-line data acquisition, finite elements and dynamic property analysis functions. With the integration and modular design idea, this SHM system can realize the data exchange and results comparison from on-site monitoring and FEM effectively. The analysis of the monitoring data collected during the process of construction shows that the system works stably, realize data acquirement and analysis effectively, and also provides measured basis for understanding the structural state of the construction. Meanwhile, references are provided for the future automates construction monitoring and implementation of high-rise building structures.

In this paper a method of finding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and $H_{\infty}$ norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.

The main purpose of this research is to utilize simple mathematical models to depict the vibration behavior and the resulted sound field of a piezoelectric disk for ultrasonic transducers. Instead of using 1-D vibration model, coupled effect between the thickness and the radial motions was considered to be close to the real vibration behavior. Moreover, Huygens-Fresnel principle was used in both incident and reflected waves to analyze the sound field under obstacles in finite distance. Results of the tested piezoelectric disk show that, discrepancies between the simulation and experiment are 2.5% for resonant frequency and 12% for resulted sound field. Therefore, the proposed method can be used to reduce the complexity in modeling vibration problems, and increase the reliability on analyzing piezoeletric transducers in the design stage.

Changes in substructure conditions, such as ballast fouling and subgrade settlement may cause the railway quality deterioration, including the differential geometry of the rails. The objective of this study is to develop and apply a hybrid cone penetrometer (HCP) to characterize the railway substructure. The HCP consists of an outer rod and an inner mini cone, which can dynamically and statically penetrate the ballast and the subgrade, respectively. An accelerometer and four strain gauges are installed at the head of the outer rod and four strain gauges are attached at the tip of the inner mini cone. In the ballast, the outer rod provides a dynamic cone penetration index (DCPI) and the corrected DCPI (CDCPI) with the energy transferred into the rod head. Then, the inner mini cone is pushed to estimate the strength of the subgrade from the cone tip resistance. Laboratory application tests are performed on the specimen, which is prepared with gravel and sandy soil. In addition, the HCP is applied in the field and compared with the standard dynamic cone penetration test. The results from the laboratory and the field tests show that the cone tip resistance is inversely proportional to the CDCPI. Furthermore, in the subgrade, the HCP produces a high-resolution profile of the cone tip resistance and a profile of the CDCPI in the ballast. This study suggests that the dynamic and static penetration tests using the HCP may be useful for characterizing the railway substructure.

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

This study presents probabilistic-based damage identification technique for highlighting damage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define the scatter signals of different paths. The energy of scatter signals till different times were calculated by taking root mean square of the scatter signals. For each pair of parallel paths an error function based on the energy of scatter signals is introduced. The resultant error function then is used to estimate the probability of the presence of damage in the monitoring area. The presented method with an autofocusing feature is applied to aluminum plates for method verification. The results identified using both simulation and experimental Lamb wave signals at different central frequencies agreed well with the actual situations, demonstrating the potential of the presented algorithm for identification of damage in metallic structures. An obvious merit of the presented technique is that in addition to damages located inside the region between transducers; those who are outside this region can also be monitored without any interpretation of signals. This novelty qualifies this method for online structural health monitoring.

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.

In this study, the effect of temperature variation on the wireless impedance monitoring is analyzed for the tendon-anchorage connection of the prestressed concrete girder. Firstly, three impedance features, which are peak frequency, root mean square deviation (RMSD) index, and correlation coefficient (CC) index, are selected to estimate the effects of temperature variation and prestress-loss on impedance signatures. Secondly, wireless impedance tests are performed on the tendon-anchorage connection for which a series of temperature variation and prestress-loss events are simulated. Thirdly, the effect of temperature variation on impedance signatures measured from the tendon-anchorage connection is estimated by the three impedance features. Finally, the effect of prestress-loss on impedance signatures is also estimated by the three impedance features. The relative effects of temperature variation and prestress-loss are comparatively examined.