Due to the increasing requirements in seismic design codes and the performance deterioration of structures during the long-term service, seismic performance of existing buildings will no longer meet the design requirements. It is imperative to conduct seismic retrofit design. However, the current retrofit design method usually needs a large number of repeated iterations of nonlinear dynamic calculation, increasing the design difficulty and reducing its applicability. A performance-based seismic retrofit design method is proposed for the existing buildings based on energy balance, which is applicable for structural retrofitting with energy dissipation devices. It considers the performance target of multiple seismic levels without nonlinear dynamic calculation and few iterations during the design. A benchmark steel frame is retrofitted by the proposed method, and nonlinear dynamic analysis is performed to evaluate the performance of the original and retrofitted structures. The results show that the drift ratio of all floors is significantly reduced after retrofitting, and the average decreases for service level earthquake (SLE), design basis earthquake （DBE） and the maximum considered earthquake （MCE） are about 20%, indicating that the seismic performance is significantly improved by retrofitting. The inter-story drift ratios of the retrofitted structure under SLE, DBE and MCE are consistent with the design values, illustrating that the expected inter-story and roof drift ratio targets are achieved, which verifies the effectiveness of the proposed method. The damper designed by the proposed method remains elastic state overall during frequent earthquakes, and enters yielding state during DBE and MCE, realizing the expected energy dissipation mechanism. The ductility demand of the damper is evenly distributed along the height, which further indicates the rationality of the design method. The presented research can provide important reference to the seismic retrofitting design of the existing building structures.

The floor acceleration amplification (FAA) reflects the amplification effect of the main structure on the ground acceleration, but the current specification and research mostly rely on historical floor records or finite element analysis results. When complex building structures are involved, the results of calculation analysis and actual earthquake damage are often different. Based on the recorded data of the shaking table test of the scaled model of the integral structure, the FAA distribution of prototype structures along the height are obtained by fitting analysis. Using the shape model of quasi seismic response spectrum, the FAA distribution surface with a certain guarantee rate is obtained, and the corresponding calculation formulas for different structural types are proposed respectively. It can better reflect the FAA distribution characteristics of complex building structures compared with the specification. Finally, the influence of site predominant period change on FAA distribution characteristics of this type of building structure is qualitatively explored.

Resilience evaluation of infrastructure system provides an essential basis for the resilience enhancement and disaster mitigation of urban communities. This study presents a seismic resilience evaluation model for water supply network that integrates the hydraulic with water quality simulation. The seismic damage scenarios of water supply network are generated according to seismic fragility model of pipelines and the Monte Carlo simulation, and the post-earthquake recovery of pipeline damages is simulated by dynamic importance-based recovery sequence. The negative influence of earthquake hazard on water quality is investigated by the chlorine concentration reduction of water, which depends on the changes in water age and supply path caused by pipeline damages. The water supply of user nodes without quality deterioration after earthquakes is used as the water quality performance index of water supply network. The proposed evaluation model is implemented in two benchmark water supply network with different layouts. Application results show that the water losses of pipeline damages lead to water flow increases and greater chlorine concentration in its upstream pipelines, and lead to prolonged water supply path and smaller chlorine concentration in its downstream pipelines. The water quality resilience of water supply network tends to be lower than that of hydraulic services, and the relative difference between hydraulic and water quality resilience is affected by the layout and operating rules of water supply network. In the application case of water supply network, the relative difference of seismic resilience loss calculated by water quality and hydraulic ranges from 17% to 286%, and there is a large difference between hydraulic and water quality resilience of water supply network whose operating rules are complex and contain regulating tanks.

The seismic reliability of water supply networks refers to its capacity to satisfy water demands of customers in the presence of potential seismic events. Currently, the majority of seismic reliability assessments for water supply networks employ Monte Carlo simulations to generate numerous seismic damage samples for evaluation. However, this approach becomes highly demanding in terms of labor and time when assessing extensive and intricate networks. To address these challenges, the equivalent scenario method is proposed as a means to enhance computational efficiency. The proposed method involves the generation of posterior probabilities using seismic damage scenarios. Subsequently, these probabilities are employed to generate equivalent scenarios. Finally, the equivalent scenarios are utilized to determine both the node reliability index and the system reliability index. The simulation results obtained using the proposed algorithm are then compared with those obtained using the traditional Monte Carlo method.The results demonstrate the feasibility of generating equivalent scenarios based on posterior probabilities. Specifically, when evaluating the reliability of 100 equivalent scenarios at a seismic intensity of 8 degrees, a few nodes exhibit evaluation errors exceed 10%, while the evaluation errors for the remaining nodes at lower seismic intensities are below 5%. Additionally, the evaluation errors for small seismic intensities are all below 5%. Moreover, increasing the number of equivalent scenarios from 100 to 250 can further reduce the assessment error to less than 5%. Thus, the algorithm proposed in this paper ensures accurate results while enhancing computational efficiency.

A structural damage recognition framework based on a self-training semi-supervised neural network (SSNN) is proposed to solve the problem of insufficient labeled data in structural damage identification. The framework utilizes the multilayer perceptron (MLP) neural network for semi-supervised training by the self-training method. The data samples with high confidence are selected from the unlabeled data to make pseudo labels, expanding the training set. Normalized frequency change ratio and damage signature index are employed as input features of neural networks to identify structural damage. Firstly, the theory fundamentals of semi-supervised self-training learning are introduced. Secondly, the procedure of structural damage identification based on self-training semi-supervised learning, including neural network construction, damage characteristic extraction, and classifier evaluation, is introduced. Finally, the proposed damage identification method is illustrated by numerical simulation of a spatial truss and experimental data of a three-story frame. The results show that the self-training semi-supervised method can expand the labeled sample data by selecting samples with higher confidence from unlabeled data, thus providing sufficient labeled data for damage identification. Under the insufficient labeled data conditions, the SSNN performs better than MLP. Compared with MLP, SSNN increases the identification accuracy by 4% and 9% under the single and two positions damage locations, respectively.

In order to improve the structural damage identification effect based on vibration signal, a structural damage identification method based on the combination of correlation function and convolutional neural network is proposed. Taking a railway steel girder bridge structure as an example, firstly, the signal-to-noise ratio of the vibration signal is improved by performing autocorrelation calculation on the vibration response of the structure, then the autocorrelation sample is used as the input of convolutional neural network, which can significantly improve the recognition accuracy. When the noise level in the vibration signal is higher, the improvement effect of the recognition accuracy of the autocorrelation sample as the convolutional neural network input is more obvious, and the autocorrelation operation has stronger noise immunity than that of the fast Fourier transform. The cross-correlation function is used to fuse the data of the multi-sensors arranged on the structure, then the fused signal is used as the input of the convolutional neural network. Under the premise of effective fusion of the data characteristics of the two sensors, the cross-correlation can double the dimension of the data set and reduce the number of parameters of the network operation, thereby reducing the time and improving the training efficiency, and the cross-correlation sample as the network input also has high recognition accuracy and strong noise immunity.

To identify structural damage efficiently and accurately, a hierarchical identification method of structural damage based on structural dynamic characteristics and deep belief network is proposed by combining machine learning with intelligent algorithm, and the damage position and degree are identified in turn. In order to identify the damage location, a 6-element vector is established by using the first three vertical vibration frequencies of the structure and the third modal displacement of a single node, and the damage location is identified by using the 6-element vector as input parameters. To identify the damage degree, the first three natural frequencies and modal displacements of vertical vibration or the 6 nodes modal curvature differences are used as parameters to input the depth confidence network to identify the damage degree. A simple-supported beam is taken as a model to verify it. It is shown that the damage position recognition accuracy can reach 100% even if the noise level reaches 10%. When identifying the damage degree, the deep belief network based on six-node modal curvature difference has strong noise resistance. The maximum relative error of damage degree prediction is less than 5.08% and the mean square error is 0.4878 under the noise of 15%. Compared with BP neural network, the prediction ability of BP neural network is better than that of deep belief network when there is no noise. Under the same noise level, the prediction ability of depth belief network is obviously better than that of BP neural network, which shows that the hierarchical identification method of structural damage based on structural dynamic characteristics and deep belief networks has strong robustness and high accuracy of identification results.

In order to get rid of the problems of inaccurate instantaneous frequency (IF) estimation and energy dissipation inside the local maximum synchrosqueezing transform algorithm, an improved method is proposed and named as local maximum synchrosqueezing transform (ILMSST). Firstly, multiple iterations are performed on the obtained IF positions to get more precise IF positions. Secondly, the IF positions where maximum values of STFT coefficients appear are searched and then moved up and down to pre-estimate IF bands. After that, the STFT coefficients outside the pre-determined IF bands are totally assigned to zero. Finally, the IF positions that correspond to local maximum values of STFT coefficients are found out and subsequently an operation of reassignment is performed on the obtained IF positions to get refined IF bands. To verify the effectiveness of the proposed method, two numerical cases and two tests on a seven-story reinforced concrete shear wall structure and a steel cable with time-varying tension forces are investigated. The results demonstrate that the proposed ILMSST method behaves better than current local maximum synchrosqueezing transform. Moreover, it not only enhances the accuracy of IF estimation but also improves time-frequency energy concentration.

Due to the problem that the tuned mass damper (TMD) system is easy to be off-tuned when applied to light structures, which leads to the decline of vibration reduction effect, a new shape memory alloy semi-active TMD system is designed in this paper. The system uses steel cables to suspend the mass to bear all its weight. Large shape memory alloy bars with rectangular effective cross sections are used to provide different bending stiffness in the two directions of the TMD system in the horizontal plane. In order to study the semi-active performance of the system, a full-scale shape memory alloy TMD system was subjected to free vibration tests in this paper. By changing the working temperature of the shape memory alloy, the influence of temperature change on the frequency and damping ratio of the TMD system was studied. The test results show that by controlling the working temperature of the shape memory alloy from -40 ℃ to 80 ℃, the frequency of the TMD system shows an increasing trend with temperature increases, while the damping ratio shows a decreasing trend with the temperature increases. The results show that the new shape memory alloy semi-active TMD system is applied to controlled structures, and once the TMD is off-tuned, it can be re-tuned by changing the temperature of the shape memory alloy. Therefore, the new shape memory alloy TMD system designed in this paper has certain engineering application value and prospect for the study of light structure vibration reduction.

Aiming at the demand of seismic protection of free-standing objects, a pendulum isolation device based on universal balls was developed. The structural characteristics and operation principle were introduced, and the design of an isolation device for protecting a pagoda could serve as a case study. Then the finite element software was used to analyze the bearing capacity between the universal ball and the disc. The accuracy of the damping mechanism analysis model was verified by tests. The isolation effect of the device under the eight-degree extremely rare earthquakes was analyzed by time history method, and was compared with the traditional friction pendulum. The results show that the isolation device not only has simple structure and reliable performance, but also has the characteristics of low friction coefficient, small acceleration threshold for triggering slip and flexibly adjustable damping mechanism. The carrying capacity of the node-to-surface contact can meet the design requirement. By means of viscous damper at one end, the damping mechanism shows obvious velocity correlation, and damping force increases with the increase of velocity. The device can achieve the isolation target while satisfying the displacement limit. Compared with the traditional type, when the displacement and acceleration response are limited, the device can achieve better comprehensive isolation effect under various seismic conditions by reasonably adjusting parameters of the damping mechanism.