In general, uplift causes changes in the structural system making its behavior and dynamic characteristics very different to common soil-structure models where no uplift is applied. Changes in rotational stiffness and lateral stiffness of structures, variations in radiation damping as well as the effective damping of structure are among the examples of these changes. Many valuable studies have been carried out in the past years about seismic control of structures with tuned mass damper (TMD) in case of two-dimensional shear structures with few performed in case of three-dimensional shear buildings. More realistic and complex models should be used in evaluating the seismic performance and design of controllers to simulate the actual behavior of buildings with higher accuracy. In this research, a three-dimensional finite element model has been created in OpenSees software with completely nonlinear and updated behavior of soil-structure system. The effects of uplift on soil-structure system equipped with TMD have been assessed using it. The conditions of employing TMD with variable stiffness have been evaluated with respect to the process carried out in this research. According to the results, based on the type of soil the structure has been designed based on, the uplift of structure can be reduced by installing TMD while it cannot be reliably used for reducing displacement and lateral acceleration of the structure. Finally, evaluation of other responses of the structure related to damages to the structure revealed the good performance of TMD.

Reinforcement corrosion affects the existing concrete structures, particularly in the coastal regions. One of the effects of corrosion of reinforcement is degradation of the bond stress that can be developed between the reinforcement and the surrounding concrete and this in turn affects the capacity of the reinforced concrete member. Prediction of the bond stress applicable for the corroded reinforcement has been attempted using analytical, empirical and soft computing methods. This article presents the comparative performance of two data-driven tools, artificial neural network (ANN) and decision tree (DT) for the task of prediction of bond stress from the corrosion level, the compressive strength of concrete and the ratio of cover and diameter of reinforcement bar. From the extensive evaluation of performance with both quantitative and graphical methods, it was concluded that the ANN approach would be better suited for the application, with the available data. For development of the models 8-fold cross validation scheme was adopted due to the limitations of data. The ANN models trained with pull-out test data, when employed with ensemble approach in predictive mode for a different experiment setup and bond strength test (flexural) data, could produce results comparable to ANN models trained with flexural test data (reported in literature). The inclusion of the additional factors (compressive strength of concrete and the ratio of cover and diameter of reinforcement bar), 8-fold cross validation approach, and ensemble prediction could be the possible reasons for achieving such portability of pull-out test based model for prediction of flexural test data.

As civil infrastructure continues to age, the extension of service life has become a financially attractive solution due to cost savings on reconstruction projects. Efforts to increase the service life of structures include non-destructive evaluation (NDE) and structural health monitoring (SHM) techniques. Nonetheless, visual inspection is more frequently used due to high equipment cost from other techniques and federal biennial inspection requirement. Recently, low-cost Radio Frequency Identification Devices (RFID) have drawn attention for crack monitoring; however, it was yet to be implemented in the field. This paper presents a crack monitoring algorithm using a developed RFID-based sensing system employing machine learning under temperature variations for field implementation. Two reinforced concrete buildings were used as testbeds: a parking garage, and a residential building with crumbling foundation phenomenon. An Artificial Neural Network (ANN)-based crack monitoring architecture is developed as the machine learning algorithm and the results are compared to a baseline model. The results show promise for field implementation of crack monitoring on building structures.

In this paper, the results of experimental work on the required energy for the crack propagation (fracture energy), rupture modulus and compressive strength of fiber-reinforced cementitious composite (FRCC) with different types of fibers after exposure to 20℃, 100℃ and 300℃ are investigated. The experimental part of the work is divided into the following stages: the effects of sub-elevated temperatures and fiber types on the fracture and mechanical behaviors of FRCC; finding a relation between the fracture energy and mechanical properties of the specimens based on I-optimal design of response surface methodology (RSM-I-optimal). Specifically, the analysis of variance (ANOVA) was examined to evaluate the influences of compressive strength and rupture modulus on the required energy for the crack propagation. For this purpose, three monotype fiber reinforced mixes have been prepared. The utilized fibers were aramid, basalt and glass. Additionally, the predictive efficiency of the RSM model was studied based on the normalized goodness-of-fit statistics (Nash & Sutcliffe coefficient of efficiency, NSE). The main finding was that both compressive strength and rupture modulus had considerable influences on the fracture energy. However, the effect of rupture modulus was far greater than compressive strength. In terms of NSE value, the model predictive efficiency was good for fracture energy.

Based on the commonly used lead rubber bearing (LRB) and sliding rubber bearing (SRB), a novel sliding lead rubber bearing (SLRB) is introduced. The mechanical properties of the three types of bearings were investigated by experiment. After that, a simply supported girder bridge with a 1/4 scale ratio was designed and fabricated, and the dynamic characteristics and seismic response of the bridge equipped with the above three types of bearings were studied. Results show that the girder's acceleration response has been effectively reduced by setting bearings only for relatively high earthquake intensity. Compared with LRB and SRB, SLRB works with more compositive seismic isolation effect. The "slide" action of the telflon-stainless-steel interface in SLRB can significantly reduce the acceleration response of girder, while the relative displacement between the pier and girder for this novel bearing is not increased due to the occurrence of collision in the bearing.