In this paper, cables are proposed to connect the inerter and main frame for translation-to-rotation conversion, i.e., the cable-bracing inerter system (CBIS), with a magnified mass and enhanced damping effect. This novel configuration has the benefits of deformation relaxation at the connecting joints, easy installation, and an adaptive layout for nonconsecutive-story deployment. Dynamic motion equations were established for a single degree-of-freedom (SDOF) model equipped with a CBIS. The influence of dimensionless parameters, such as inertance-mass ratio, stiffness ratio and additional damping ratio on vibration mitigation were studied in terms of displacement response and force output. A single objective and multiple objective optimal design method were developed for a CBIS-equipped structure based on a performance-oriented design framework. Finally, the mitigation effect was illustrated and verified by a numerical simulation in a time-domain. The results showed that a CBIS is an effective structural response mitigation device used to mitigate the response of structural systems under earthquake excitation. Using the proposed optimization method, CBIS parameters can be effectively designed to satisfy the target vibration control level.

Dynamic analysis of structures is very important for structural design and health monitoring. Conventional numerical or experimental methods often suffer from the great challenges of analyzing the responses of linear and nonlinear structures, such as high cost, poor accuracy, and low efficiency. In this study, the recurrent neural network (RNN) and long short-term memory (LSTM) models were used to predict the responses of structures with or without nonlinear components. The time series k-means (TSkmeans) algorithm was used to divide label data into different clusters to enhance the generalization of the models. The models were trained with different cluster acceleration records and the corresponding structural responses obtained by numerical methods, and then predicted the responses of nonlinear and linear structures under different seismic waves. The results showed that the two deep learning models had a good ability to predict the time history response of a linear system. The RNN and LSTM models could roughly predict the response trend of nonlinear structures, but the RNN model could not reproduce the response details of nonlinear structures (high-frequency characteristics and peak values).

Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.

The construction industry's urgent need for sustainable alternatives to ordinary Portland cement (OPC) has spurred significant interest in supplementary cementitious materials (SCMs). Therefore, this study investigates the synergistic effects of rice husk ash (RHA) and extracted microsilica (EMS) as partial replacements for cement in high-strength concrete (HSC). RHA was produced by controlled combustion of rice husk (RH) at 600-700 °C, while EMS was obtained through an optimized extraction process. Along with control mix (CM), three ternary mixes incorporating 15-35% RHA and 5-15% EMS were investigated to evaluate their effects on the mechanical and durability properties, as well as microstructural characteristics. Experimental results demonstrated that samples containing 20% (5% EMS + 15% RHA) and 35% (10% EMS + 25% RHA) as cement replacement exhibited enhanced performance, increasing the 28-day compressive strength by 11.56% and 5.98%, respectively, as compared to conventional concrete. Durability assessments of the optimal mixes revealed a significant reduction in water absorption (WA) and permeability. Physiochemical characterization confirmed the increased formation of dense calcium silicate hydrate (CSH) gels and a reduction in portlandite content, indicating high pozzolanic reactivity. However, the 50% replacement mix (15% EMS + 35% RHA) showed inferior performance due to the particle agglomeration and higher porosity. This study shows that the strategically formulated combinations of EMS and RHA blends (up to 35%) can produce sustainable HSC with improved mechanical properties and durability while mitigating environmental impacts. These findings provide valuable insights for advancing eco-friendly construction materials, supporting global sustainability initiatives in infrastructure development.

A grout sleeve connection is a typical kind of joint in prefabricated structures. However, for construction and manufacturing reasons, defects in this kind of joint are usually inevitable. The joint quality of a prefabricated structure has a significant influence on its overall performance and can lead to structural failure. Due to the complexity of various types of materials used in grout sleeve connections, traditional non-destructive testing methods, such as Acoustic Emission (AE), Ultrasonic Testing (UT), Guided Wave Testing (GW), are facing great challenges. The recent development of deep learning technology provides a new opportunity to solve this problem. Deep learning can learn the inherent rules and abstract hierarchies of sample data, and it has a powerful ability to extract the intrinsic features of training data in complex classification tasks. This paper illustrates a deep learning framework for the identification of joint defects in prefabricated structures. In this method, defect features are extracted from the acceleration time history response of a prefabricated structure using a convolutional neural network. The proposed method is validated by vibration experiments on a half-scaled, two-floor prefabricated frame structure with column rebars spliced by different defective grout sleeves.