In this paper, a total kernel density estimation (KDE)-based nonparametric probabilistic seismic loss assessment framework for structures is proposed, and the cloud-point strategy and stochastic nonstationary excitation are well incorporated during the procedure. The methodology framework and implementation steps are first introduced in detail, and then an application example of a reinforced concrete frame is adopted to verify the feasibility of the total KDE-based nonparametric framework. A comparison is performed via the classic parametric least-square regression (LSR) approach, and lognormal distribution is also adopted during the analysis. Moreover, the classic Monte Carlo Simulation (MCS) approach is adopted as the benchmark for validation. In general, the KDE-based framework is nonparametric without predefined assumptions of generated curves, making it an effective nonparametric method for probabilistic seismic loss estimation and statistical prediction of structures. Use of the cloud-point strategy greatly improves the calculation efficiency, and the stochastic nonstationary excitation effectively guarantees the real situation of load input. From the application example, the high fidelity and great efficiency of the KDE-based framework (fragility, damage, loss, and other parameters) are suggested in probabilistic seismic analysis. Thus, the total KDE-based nonparametric framework provides a new perspective for further development and in-depth investigation in probabilistic seismic loss research. © 2025 Elsevier Ltd

Modelling complex engineering structures involves numerous parameters that are difficult to determine. Many uncertainties in the model parameters cannot be resolved through standards and experiments alone, necessitating model updating methods. The Bayesian model updating method is one of the most popular approaches for this purpose; and it has led to the development of numerous improved algorithms. However, the traditional Bayesian model updating algorithms are time-consuming and may not always yield the most likely posterior distributions of the model parameters in engineering applications. Therefore, this paper introduces a refined transitional Markov chain Monte Carlo (rTMCMC) algorithm based on the TMCMC algorithm and improved TMCMC (iTMCMC) algorithm. The rTMCMC algorithm is an adaptive Bayesian model updating method designed for engineering applications; it can adaptively find the most likely posterior distributions of model parameters without increasing the computation time. The efficiency of the rTMCMC algorithm is validated via a numerical example, which compares it with the TMCMC and iTMCMC algorithms. Finally, two examples at both the component and structural levels, updated by the rTMCMC algorithm, and compared with the iTMCMC algorithm, are presented, demonstrating the effectiveness of the rTMCMC algorithm in engineering applications.

Groundwater infiltration through openings in tunnel linings can lead to the migration of fine soil particles from the coarse particle framework due to seepage forces known as internal erosion. However, current numerical methods face limitations in capturing the key process of the fluidization of fine particles surrounding tunnel linings within acceptable computational costs. To address these challenges, we introduce an erosion law into the two-point material point method where (i) the initiation of the fluidization of fine particles is based on the relative velocity between the solid and fluid phases and (ii) the fluidized soil mass is transferred to the fluid phase and migrated by the seepage force. After the verification of the present method, the internal erosion due to tunnel leakage is analyzed, with parametric analyses of the initial fine particle content, groundwater level, internal friction angle, and opening location. The results indicate that internal erosion accelerates the soil loss during tunnel seepage, highlighting the need for significant attention regarding this issue. © 2025 Elsevier Ltd

Seismic isolation technology is widely used to mitigate the earthquake responses of the structures. However, when installed in buildings with high aspect ratios, conventional isolation bearings can easily fail due to tension caused by the overturning of the structure. This paper proposed a semi-active-controlled anti-tension device for base-isolated buildings, which consists of several semi-active-controlled rods and a monitoring sensor. The semi-active-controlled rods can switch between the performance states according to the sensor's total length. When the bearing is in compression, the semi-active-controlled rods can extend and contract freely. When the bearing is in tension, the semi-active-controlled rods can contract freely but exhibit a large stiffness when extended. Thus, the semi-active-controlled rods resist the tension exerted on the bearings and avoid the overturning of the building. Axial cycle loading tests were carried out to study the performance of the semi-active-controlled rod. A user-defined element was developed and incorporated into the FEM software Abaqus to simulate the performance of the semi-active-controlled rod. It is found that the user subroutine developed can simulate the semi-active-controlled rod's behavior accurately. © 2025 Elsevier Ltd

Energy-dissipating damping devices installed at beam-column joints are effective in mitigating the damage to the joints during earthquakes. However, the deformation experienced by typical joint dampers is generally equal to or less than that at the beam-column joints of the structure, resulting in lower energy dissipation efficiency. To address this issue, this research proposes an innovative response-amplified friction damper (RAFD) designed to enhance the energy dissipation efficiency of conventional friction dampers (FDs) for beam-column joints in frame structures. The RAFD incorporates three rotating joints and realizes the leveraging amplification mechanism through a rational design of its component dimensions. The rotational amplification factor, defined as the ratio of the rotational angle between the joints of the RAFD and conventional FD, is derived to investigate the hysteresis performance of the RAFD. To evaluate the hysteretic responses of the RAFD, three full-scale prototype specimens were designed, fabricated, and tested under two different loading schemes. Experimental results indicate that the RAFDs exhibit stable cyclic performances with negligible damage. Moreover, the RAFDs exhibit special asymmetric flared hysteresis curves, which are significantly determined by their geometric dimensions. Furthermore, the study reveals that the distance from the pin to the adjacent connected plate positively impacts the bearing capacity and the rotational amplification factor of the RAFD. Conversely, increasing the spacing between the two pins has a negative effect on these parameters. The installation type also critically affects the bearing capacity, i.e., convex RAFDs demonstrate a greater bearing capacity compared with that of concave RAFDs. © 2025 Elsevier Ltd

Conventionally, the design and assessments of structures would involve a large number of structural analyses. This process is usually realized by refined finite element models, which is extremely time-consuming. Recent advancements in the data-driven deep learning (DL) models have opened a viable avenue for forecasting time history responses. Nevertheless, most existing DL surrogate models treat the load as the sole input parameter, neglecting the impact of structural properties. Due to this limitation, it becomes impossible to achieve uncertainty quantification of response that considers stochastic structural parameters. Under this circumstance, two methods are proposed to incorporate structural parameters and ground motions (GMs) into input sources for predicting seismic responses in the present study. In this way, uncertainties within both structural parameters and GMs could be considered during uncertainty quantification of seismic response. The proposed methods’ feasibility was verified through a numerical case study involving a typical reinforced concrete frame structure. Furthermore, two benchmark methods that exclude structural parameters as input sources were employed to compare. Additionally, the application of seismic reliability analysis on the basis of these methods is elucidated. The results show that the proposed methods not only enhance the prediction precision and robustness of surrogate models compared with two benchmark methods but also achieve uncertainty quantification of seismic response considering uncertainties in structural parameters and GMs. © 2025 Institution of Structural Engineers

Seismic resilience is a critical index in the earthquake engineering. In recent years, it has received extensive attention and has been broadly used in post-earthquake assessments, especially for the structures after strengthening. At this stage, the seismic resilience is commonly analyzed using deterministic approaches, while the corresponding probabilistic resilience assessment still needs further research. In this paper, a probabilistic resilience assessment framework of structures considering the functional uncertainty is proposed, and a case study for external prestressed subframe in seismic strengthening is performed. The probabilistic resilience assessment framework consists of the probabilistic analyses of fragility, expected loss, residual functionality, recovery time and resilience index, and the resilience developments along with the intensity level or recovery time are detailedly discussed in the procedure. Subsequently, an implementary example of the existing frame strengthened by an external prestressed subframe is given, and the proposed probabilistic resilience assessment framework is performed for comprehensive analyses. In general, the functionality varying with recovery time presents significant uncertainty for each scenario and intensity level, which proves the necessity of resilience analyses in a probabilistic way. With the increase of prestress level, the obtained mean resilience index increases, and the resilience exceeding probability curves move upward for all the conditions, which signifies the superiority of a higher prestress level to increase the seismic resilience and to improve the risk-resistant capacity in external strengthening. In a sense, the probabilistic resilience assessment framework evaluates the resilience development from an uncertain perspective, which provides a significant reference for the subsequent probabilistic risk analyses in engineering structures.

This paper presents a multi-hazard risk analysis of high-rise buildings exposed to earthquakes and strong winds. A concurrent hazard database was first collected, consisting of 35,687 sets of concurrent hazards from 1901 to 2020, with earthquakes greater than M 4.0 and wind speeds exceeding 10 m/s. The probability of simultaneous occurrence of earthquakes and strong winds was theoretically derived and verified through Monte Carlo simulation and statistical result. Afterward, numerical simulation was performed on two high-rise buildings, with special focus on the fragility of the structures exposed to both individual and multiple hazards. The maximum top displacement of the structure under multiple hazards exceeded 0.9 %-24.6 % of the superposition of responses under individual hazards. The annual failure probability of the structure was analyzed through convolution of the disaster risk function and the structure fragility function. It was indicated that the annual failure probability under concurrent hazard conditions was 1.12-2.05 times of that under individual hazard conditions in the damaged state of IDR (Inter-story Drift Ratio)> 1.5 %.

Seismic damage to building components and structures results in varying degrees of degradation in performance and safety. Accurate and rapid predictions of future performance and residual capacity of damaged structural components are crucial to post-event recovery efforts. Traditional assessment methods rely heavily on experts who exercise relatively coarse and qualitative evaluation methods. This setup significantly impacts the efficiency and accuracy of structural condition assessments at regional scales. The present study introduces a workflow that integrates computer vision and deep learning tools for rapid, accurate, and quantitative assessment of structural damage. The proposed end-to-end automated damage assessment workflow can derive the residual capacities of structural components (including stiffness degradation ratio, strength degradation ratio, and damage ratio) from conventional images that can be collected by most of the common handheld devices. The workflow features two custom-designed deep learning modules, a transfer learning-based feature extraction network and a multi-task learning-driven multiple regression prediction network. A database comprising 108 laboratory-scale quasistatic experiments on masonry wall specimens is used for training and testing. The results demonstrate the proposed workflow's effectiveness in quantifying residual capacities such as R2strength = 0.993, R2stiff = 0.987, R2damage = 0.987. The workflow can be further improved by employing more advanced models and utilizing a more comprehensive annotated dataset. It can be deployed by both experts and through crowdsourcing in postevent scenarios.

The current performance-based seismic evaluation methodologies can be broadly classified into three categories based on the adopted indicators: displacement-based seismic evaluation, energy-based seismic evaluation, and damage-based seismic evaluation. However, these approaches predominantly concentrate on analyzing either the entire structure or individual components, whereas a clear computational methodology for evaluating the performance from the material level to structural level is still lacking. With the ongoing advancements in finite-element analysis and computer science technologies, it has become feasible to conduct more sophisticated and multilevel seismic performance evaluations of structures. Acknowledging that damage at the material level underlies the degradation of performance in both components and structures as a whole, this paper further establishes a multilevel hierarchical damage indicator framework based on quantitative material damage, spanning from the material level to the structural level. Additionally, a seismic performance evaluation is carried out on a specific frame structure utilizing incremental dynamic analysis (IDA) and the fragility analysis method, for the purpose of verifying the effectiveness of the multilevel damage indicators in structural assessment. In general, the damage indicator presented in this paper allows for the assessment of damage at multiple levels, offering a more comprehensive reflection of damage and providing broader application prospects in the future. © 2024 American Society of Civil Engineers.