The slender structure is prone to be affected by horizontal force; therefore, the seismic performance needs to be considered carefully. Meanwhile, due to the low cost and good performance on the seismic resistance of the Tuned liquid dampers (TLDs) system, it has been widely used for vibration control. Regarding the abovementioned background, in this study, we conduct the experiment to investigate the seismic performance of the slender structure with the integral water tank, and two designed parameters (the placement location and the water level of the water tank) are studied. The experimental phenomenon and the structural accelerations are recorded to be analyzed further and discussed, then a useful design guide for an integral water tank is summarized. Finally, some practical and helpful advice and conclusions are put forward for the design of the water tank that is used for the purpose of seismic resistance in the slender structure. Our research can fill the blank in the research on the integral water tank of TLDs system, which also has good potential to achieve the enhancement of slender structure seismic performance.

Recently, the light-type pier can be easily observed in urban elevated roads and bridge structures. This type of pier is broadly used in the field of structural engineering. The Thin-Walled Steel-Reinforced Concrete Pier (TSRCP), as the typical pier of the light-type piers, shows a better mechanical performance than the conventional reinforced concrete pier due to the enhancement of the H-shape steel. In terms of the TSRCP, the designed parameters, including the depth–width ratio (DWR) and axial compression ratio (ACR), have been determined in this study as the concerned variables. Then, a low cyclic loading experiment is conducted based on the three test specimens to investigate the effects of the concerned variables on the failure of the specimens. Meanwhile, some comparative studies are carried out based on the failure processes and modes, critical loading values (cracking, yielding and ultimate state), strain and ductility. The obtained experimental results demonstrate that all specimens illustrate a bending failure mode under the vertical and horizontal low cyclic loadings. Furthermore, for the concerned variables, the increasing DWR will reduce the ultimate bearing capacity of the TSRCP but enhance the plastic deformation. For the ACR, the decrease positively affects the cracking load, further improving the ultimate bearing capacity; however, the deformation capacity will be restrained. Finally, Abaqus is adopted to model the failure processes of three specimens; the comparative study has been conducted based on the simulated and experimental results. After that, the effects of the concerned variables on the failure performances are discussed. Meanwhile, the horizontal ultimate bearing capacity calculated equation for the TSRCP was deduced and validated based on the experimental and simulated data; the verification proves that the proposed equation can obtain a good result. In this study, the complex calculated process of the original equation can be simplified, which not only provides a convenient and useful way to design and manufacture this type of component but can serve as a guideline for validating the practical engineering applications.

Structural health monitoring and damage identification aim to detect the internal damage and evaluate the health conditions of the practical engineering structure, which has been the most popular research field for several decades. The sensitivity‐based method incorporated with the regularization techniques is the classical and useful approach, and it can obtain accurate damage detection results. However, with the development of civil engineering structures, this classical method faces two problems: one is it is only applied to simple structures rather than full‐scale structures, and second is the iterative calculation efficiency is lower. Therefore, aiming at these drawbacks, the two improvement strategies have been introduced to the original method for its enhancement in the application potential and computational efficiency. The proposed method has been verified based on two examples, i.e., a numerical steel truss with 144 elements and a full‐scale experimental steel truss with 160 elements. The results prove that the proposed method has better efficiency and good application potential in the practical full‐scale engineering structure.

The academic community has paid much attention to the research topic of structural health monitoring (SHM) and damage identification for several decades, which flourishes the development of diverse damage identification approaches. The sensitivity‐based damage identification enjoys popularity due to its clear physical meaning and long development history. However, this type of method faces challenges of fewer applications on the large‐scale structure, slow convergence rate, and poor performance based on the sensitivity of the modal parameters. Aiming at the existing obstacles, this study enables to propose a novel method based on time series analysis model and improved sparse regularization technique for damage identification of the large‐scale structure. Firstly, the model reduction technique is introduced to condense the unconcerned degrees of freedom (DOFs) of the finite element model (FEM) of the large‐scale structure, and then the sensitivity of the autoregression (AR) coefficient with respect to the damage coefficient for the large‐scale structure has been deduced, which can reduce the uncertainty, which comes from modal identification, in modal parameter sensitivity. Furthermore, the mean‐value normalization strategy is incorporated into the sparse regularization solving process to improve the computational efficiency. The proposed method has been validated based on a numerical continuous rigid frame bridge and an experimental steel truss bridge. Compared to the moth‐flame optimization (MFO) algorithm and traditional regularization methods, for both noise‐free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. Furthermore, regarding the numerical example, the damage identification results show that the max identification errors of the proposed method are 6% among the three cases under noise‐free and noise‐polluted data, while the max identification errors for MFO and the regularization method are 15% and 10%, respectively. And for the experimental example, based on the limited sensors, the proposed method can accurately detect the damage location and quantify damage severity with a good accuracy, which means this method has good potential for actual engineering structure. To summarize, the findings highlight that the AR coefficient sensitivity combined with improve sparse regularization can effectively detect damage of large‐scale structure, providing a feasible way to expand the application of the sensitivity‐based method.