Accurately capturing the full-range response of structures is crucial in structural health monitoring (SHM) for ensuring safety and operational integrity. However, limited sensor deployment due to cost, accessibility, or scale often hinders comprehensive monitoring. This paper presents a novel data fusion framework utilizing diffusion models, to reconstruct the full-range structural response from sparse and heterogeneous sensor measurements. We incorporate Diffusion Posterior Sampling (DPS) into the reconstruction framework, using sensor measurements as probabilistic constraints to guide the sampling process. Three forward models are designed: Direct Observation Mapping (DOM), Channel-based Observation Mapping (COM), and Neural Network Forward Model (NNFM), enabling flexible adaptation to different sensor placement conditions and reconstruction targets. The proposed framework is validated on a steel plate shear wall exhibiting nonlinear responses. By simultaneously sampling 100 realizations and averaging them as the ensemble prediction result, the three forward models achieve Weighted Mean Absolute Percentage Errors of 1.62% (DOM), 3.27% (COM), and 3.49% (NNFM). Sensitivity analyses further demonstrate robust performance under varying hyperparameters, sensor configurations, and noise levels. In addition, Denoising Diffusion Implicit Model (DDIM) acceleration reduces inference time to under one second for 100 samples. The framework is also applied to a large-scale engineering case of a nuclear containment structure, demonstrating the practical workflow of applying DPS in real-world scenarios. The proposed framework shows new possibilities for probabilistic modeling and decision-making in SHM by harnessing the capabilities of diffusion models, offering a novel data fusion approach for full-range monitoring of structures.

Probabilistic full-field reconstruction provides uncertainty-aware response evidence for structural reliability assessment, yet inference from sparse and noisy measurements remains underdetermined. Most existing methods overlook shifts between offline training and operational distributions. Under such shifts, posterior intervals may become miscalibrated, causing the reported uncertainty to lose its probabilistic meaning. This study proposes Modal Residual Flow Matching with Context-Conditioned Affine Spread Transport (MoRF-AST) for calibrated structural virtual sensing under changing operating conditions. MoRF constructs an analytic Gaussian reference posterior in normalized modal coordinates and trains a conditional flow only on posterior-whitened residuals. At deployment, AST estimates response scale from historical measurements at installed sensors and uses gated, mean-preserving Bures-Wasserstein transport to adjust posterior spread. On a bridge-deck benchmark, MoRF achieves a posterior-mean normalized root-mean-square error (NRMSE) of 7.20

The deflection prediction of diaphragm walls stands as a critical aspect of safety management for excavation construction. Bayesian updating holds a prominent position among several existing prediction methods, owing to its probabilistic predictions and the ability to simultaneously consider prior knowledge and observational data. This paper develops a comprehensive Bayesian updating framework for wall deflection prediction in braced excavation, taking into account both model error associated with the prediction model chosen and measurement error. Further, both of these errors typically exhibit depth dependency and spatial correlation and thus are modeled as stochastic processes. An innovative efficient Kriging (e-Kriging) surrogate model and an Expectation-Maximum algorithm based adaptive importance sampling (AIS) method are proposed to improve computational efficiency. The prediction performance of this proposed framework is verified via an exhaustively reported Taipei National Enterprise Center (TNEC) excavation project. Moreover, this paper compares the influences of different selections of prior distributions and measurement data on the predictions. Results suggest that using only a subset of the middle portion of measurement data for updating is often a more appropriate choice.

Bayesian updating is a powerful tool for updating engineering models with observed information. As a result of structural health monitoring sensors or platforms, up-to-date information reflecting characteristics of structures and infrastructure systems is available. However, there is usually a large amount of data collected from monitoring technologies in practical engineering, which means the associated computational cost for Bayesian updating will be considerably challenging. The lack of knowledge of observed information makes it impossible to select valuable information for updating. To overcome these limitations, this paper proposes an adaptive information filtering (AIF) method based on sensitivity analysis for Bayesian updating. Specifically, observed information is classified by means of sensitivity analysis and the information valuable to the updating target is filtered out. Moreover, the dispersion of the posterior distribution is adopted as the metric for quantifying updating effectiveness. One linear algebraic example and one case study of chloride-induced concrete corrosion considering carbonation are investigated to demonstrate the computational performance of the proposed method.

Concrete structures in the Hong Kong–Zhuhai–Macau (HZM) sea link project are designed for a working life of 120 years; to ensure this length of service life, an efficient yet rational strategy for the long-term durability planning and management must be established. Herein, we comprehensively review various data-driven and model-based approaches to the long-term durability planning and management of these concrete structures. To this purpose, we constructed a smart durability database with self-cleaning and self-predicting capacities. Durability models used in the durability assessment and planning are described, together with their different combinations adapted to different scenarios. Using the constructed database and models, we developed a method for durability planning based on life cycle cost analysis and devised basic maintenance schemes and plans. Lastly, several crucial aspects related to long-term durability maintenance and planning of concrete structures were highlighted.

In existing probabilistic durability design for marine reinforced concrete (RC) structures, the material-level deterioration behavior is modelled, and corrosion onset at a point on the rebar is taken as the limit state. However, the owners may more concern the risk of the structural member's surface damage or the potential maintenance demands. To bridge the gap, a member-level durability design method is developed in this paper. It treats the target reliability index as a function of member-level target performance, and incorporates the existing design method into the proposed framework. Its differences and relations with the existing material-level method are addressed. By improving the effective tools from existing studies, the general procedures of performing a member-level durability design are proposed. Taking the HZM project as the examples, member-level durability designs are performed for its beams, columns and immerged tube tunnels under various exposure conditions with various design targets being considered. Accuracy of the design outputs are checked by assessing the corresponding member-level performance and comparing it to the design targets. In addition, the proposed method is also validated with the in-situ data from an in-service high-pile RC wharf.

Earthquake hazards may cause a significant loss to urban hospital network system (UHNS), including hospitals and medical transportation networks. As such, systematic seismic resilience assessment of such systems is of paramount importance to reduce loss and optimize casualty care during earthquakes. For that purpose, this paper proposes a novel method for assessing the seismic resilience of UHNS that considers the logical dependencies or coupling among the system’s components, such as the casualty sources, the hospitals and the medical transportation networks. A resilience index is defined as the ratio between the average “shortest” time for a casualty to receive medical treatment before and after an earthquake. The time consists of both transportation time to a hospital and queuing time at the hospital. To obtain the shortest time, a real-time simulation model is proposed that considers the casualty sources, the changes in road traffic, the updates of Origin-Destination (OD) matrix for transportation time, the modifications of waiting time in hospital, the path reselection by casualty, and the changes in functionalities of bridges, hospitals and roads. The functionalities are obtained by analyzing fragility models, calculating residual functionality and estimating functionality recovery times. To calculate the fragilities of bridges and hospitals, a refined nonlinear finite element (FE) analysis is performed based on a general FE software, OpenSees, and the damages are obtained for various structural and nonstructural components, e.g. column, stairs and CT scanner of hospitals, as well as piers, bearings and abutments of bridges. The proposed method is demonstrated in a seismic resilience assessment of a realistic UHNS in Xiamen city of China and provides valuable references to assessments of seismic resilience of UHNS.

Small-radius curved bridges are mostly used for overpass ramps, that are spatially irregular and usually have very complex seismic behavior. It is not easy to reproduce such behavior because of the need for large-scale shaking tables. The hybrid test is one of the most effective approaches for solving this problem by considering the structural elements of interest as physically tested substructure while the rest is numerically simulated. In this paper, a hybrid test system was first developed based on the OpenFresco framework, where one of the piers was considered as the tested substructure, and the rest was simulated by OpenSees. A novel spatial loading device (SLD), configured as the Stewart pattern, was then developed to achieve the boundary conditions between substructures. The control schemes to perform the force-displacement mixed control, conduct the geometric transformation while considering the load point offset, and achieve an external displacement control were proposed and validated through several rounds of hybrid testing. The experimental results indicate that the experimental system including loading control subsystem and hybrid control subsystem can realize the loading command accurately.

The swift recuperation of communities following natural hazards heavily relies on the efficiency of transportation systems, facilitating the timely delivery of vital resources and manpower to reconstruction sites. This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments, proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery. Focusing on optimizing urban population capacity in the aftermath of earthquakes, the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity. The methodology is demonstrated through an analysis of Beijing’s transportation system, elucidating how enhancements to transportation infrastructure fortify the resilience of built environments. Additionally, the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake. This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources, indicating a need for retrofitting. Through sensitivity analysis, this study explores how investments in the built environment or logistical systems can increase the resource supply rate, thereby contributing to more resilient urban areas in the face of seismic challenges.

Chloride diffusivity significantly affects the durability of coastal reinforced concrete (RC) buildings, and is one of the major concerns in practice. However, its design and conformity control are commonly based on “deem-to-satisfy” methodology, and consequently it's difficult to evaluate the building's durability performance. In this paper, a reliability-based conformity control method for diffusivity is proposed to eliminate the gap. First, the existing empirical method and semi-empirical method are reviewed, and their drawbacks are addressed according to the reliability assessment. Then, based on the statistical acceptance sampling theory and the engineering practice of Hong Kong-Zhuhai-Macau sea-link project, the reliability-based method for diffusivity is proposed, which can be applied in the construction of coastal RC buildings. In this method, effects of local environmental conditions (e.g., temperature and humidity) on chloride diffusivity are considered, and the relation between diffusion coefficient for design (Da0) and for conformity control (Dnssm) is suggested accordingly. Finally, the conformity control for the concrete's chloride diffusivity of an accessory RC building in the Shantou Harbor is taken as the example, and the schemes by empirical method, semi-empirical method and reliability-based method respectively are established. Their acceptable quality level (AQL) and limiting quality level (LQL), probabilities of acceptance and outcomes in terms of durability reliability are evaluated and compared. It is found that the reliability-based method can offer the schemes that is consistent with the design target, and reject most of the nonconforming lots without harming the producers' benefits.

Deterioration models of reinforced concrete (RC) in marine environment, to be applied to existing structures, usually need to be calibrated with long-term in-situ data. The Bayesian model updating provides a framework to incorporate measured data into existing models to make them more realistic. The measured chloride concentrations of concrete at different depths are related to each other through the Fick’s second law, but they are treated as independent in existing updating methods, which affects the accuracy of model updating. To solve this issue, this paper proposes a data-based and physics-informed (DBPI) likelihood function to incorporate the physical law behind the measured data into Bayesian updating framework, whose validity is first confirmed through numerical examples, and then it is applied to the durability assessment of an existing wharf structure in marine environment. The parameters involved in the chloride ingress model and the critical chloride concentration model are updated using the data from durability inspections. The durability performance of the structure is then assessed using the updated models, which is consistent with the actual surface deterioration observed in the two inspections. Discussion of the updated results reveals that ignoring the physical law behind the measured data may result in incorrect inferences of the chloride ingress model and multi-mode distribution of the updated parameters, which is solved by using the proposed DBPI likelihood function, and the accuracy of Bayesian updating is significantly improved.

ETHNOPHARMACOLOGICAL RELEVANCE:Huachansu (HCS) is a traditional Chinese medicine obtained from the dried skin glands of Bufo gargarizans and clinical uses of HCS have been approved in China to treat malignant tumors. The traditional Chinese medicine theory states that HCS relieves patients with cancer by promoting blood circulation to remove blood stasis. Clinical observation found that local injection of HCS given to pancreatic cancer patients can significantly inhibit tumor progression and assist in enhancing the efficacy of chemotherapy. However, the material basis and underlying mechanism have not yet been elucidated.AIM OF THE STUDY:To investigate the therapeutic potential of HCS for the treatment of pancreatic cancer in in situ transplanted tumor nude mouse model. Furthermore, this study sought to elucidate the molecular mechanisms underlying its efficacy and assess the impact of HCS on the microenvironment of pancreatic cancer. To identify the antitumor effect of HCS in in situ transplanted tumor nude mouse model and determine the Chemopreventive mechanism of HCS on tumor microenvironment (TME).METHODS:Using the orthotopic transplantation nude mouse model with fluorescently labeled pancreatic cancer cell lines SW1990 and pancreatic stellate cells (PSCs), we examined the effect of HCS on the pancreatic ductal adenocarcinoma (PDAC) microenvironment based on the transforming growth factor β (TGF-β)/Smad pathway. The expression of TGF-β, smad2, smad3, smad4, collagen type-1 genes and proteins in nude mouse model were detected by qRT-PCR and Western blot.RESULTS:HCS significantly reduced tumor growth rate, increased the survival rate, and ameliorated the histopathological changes in the pancreas. It was found that HCS concentration-dependently reduced the expression of TGF-β1 and collagen type-1 genes and proteins, decreased the expression of Smad2 and Smad3 genes, and downregulated the phosphorylation level of Smad2/3. Additionally, the gene and protein expression of Smad4 were promoted by HCS. Further, the promoting effect gradually enhanced with the rise of HCS concentration.CONCLUSIONS:The results demonstrated HCS could regulate the activity of the TGF-β/Smad pathway in PDAC, improved the microenvironment of PDAC and delayed tumor progression. This study not only indicated that the protective mechanism of HCS on PDAC might be attributed partly to the inhibition of cytokine production and the TGF-β/Smad pathway, but also provided evidence for HCS as a potential medicine for PDAC treatment.

The Huilan interchange built in Mianzhu City, China, in 2004 experienced serious damages during the Wenchuan earthquake in 2008. Due to the irregularity and eccentricity, the curved ramp bridge suffered significant spatial action from the earthquake shaking, where several short piers failed in a bending-shearing-torsional mixed damage pattern. To ascertain the reason for the failure of short piers, the seismic performance of the curved bridge was reproduced and evaluated by large-scale hybrid tests. In the hybrid tests, the curved bridge C was selected as the prototype structure to reproduce the failure scenario, for which the 3/4 scale model of pier No. 2 was physically tested by a large spatial loading system to reproduce the spatial failure mode in all six degrees of freedom. The rest of the structure was modeled by OpenSees to simulate the earthquake response. Several rounds of hybrid tests were conducted, and it was found that pier No. 2 first yielded in the flexural mode, then changed to the mixed flexural-shear mode, and finally failed in the shear mode with a torsion effect. These results are generally consistent with the post-earthquake observations.

Surface transportation systems play a vital role in supporting a region's functionalities. They are expected to remain operational before and even after a hazardous event (e.g. an earthquake). The importance is evident to estimate the post-disaster performance of traffic systems under a probability-based framework, considering the uncertainties arising from both the hazards and the transportation infrastructure fragilities. This paper proposes an explicit approach for evaluating the performance of transportation systems immediately after an earthquake event. The method estimates the spatial distribution of vehicles in the traffic network in a closed form and thus is relatively efficient compared with traditional methods (e.g. an agent-based method). The applicability of the proposed approach is demonstrated through an application to the post-earthquake performance assessment of the traffic network in Tangshan City, China, a city that suffered catastrophically from the 1976 Tangshan Earthquake. Analytical results show that the proposed method can well reflect both the temporal and the spatial variations of the traffic flow, and thus offers rational support for predicting the post-earthquake traffic scenarios and for optimizing strategies to improve the transportation capability under emergent conditions.

Multi-axial real-time hybrid simulation (ma-RTHS) utilizes multiple loading devices to realize boundary control with multiple degrees of freedom (MDOF), thus being capable of handling complex dynamic scenarios and multi-dimensional problems. In this paper, a new control technique was developed by using a parallel configuration of double shaking tables to implement shear force and bending moment at the boundary between substructures. The dynamic forces are combined by inertia forces of controlled mass driven by electromagnetic shaking tables. The two shaking tables are packaged as a boundary-coordinating device (BCD). An enhanced three-variable control (ETVC) was proposed to consider the coupling effect between two shaking tables and incorporated with the adaptive time series (ATS) compensator to improve the synchronization of the two shaking tables. The proposed control method was verified by three rounds of hybrid tests on a four-story steel shear frame using different ground motions. Nine criteria were utilized to evaluate the performance of RTHS including both tracking performance and global performance indexes. It was proved that RTHS was successfully implemented, and the boundary forces were well-tracked by the proposed control strategy. Good tracking performance was achieved to prove the effectiveness of the strategy.

To predict the future condition of a bridge, statistical models for the time-in-condition rating (TICR) can estimate the time that a bridge stays in a given condition and then predict the future condition of the bridge. However, existing research typically uses the probability density functions as the likelihood function when the TICR is estimated by Bayesian updating, in which the change in the condition rating (CR) between two consecutive inspections is assumed to occur at the later inspection, which ignores the uncertainty of the time of the condition change. This assumption will introduce an error, which is particularly significant when the two consecutive inspections are separated over a long time. In addition, a large amount of existing bridge inspection data in China has not been fully recorded; for instance, a lot of bridge inspection data only contains the CR of the bridge from the last inspection. Current research that is based on the TICR has difficulty using this incomplete data. To solve these difficulties, this paper proposes a probability-based likelihood function for the Bayesian updating of the TICR models, which could estimate the distribution of the TICR more accurately using fully recorded or single data. The accuracy of the proposed method is verified with numerical examples, and the results from different methods are discussed. Then, the effect of using complete and single data are examined. The proposed method is applied to the CR of the superstructures of reinforced concrete bridges in Beijing that uses the real inspection data, and the future deterioration risk is evaluated using the updated TICR models.

Surface chloride concentration (CS) and chloride diffusion coefficient (DCl) are key parameters for durability assessment of concrete structures in marine environment; they are time-varying and highly dependent on the exposure condition. To reasonably model their behaviors at a specific location, durability measurement data are often needed to calibrate the apparent chloride ingress model based on Fick's second law. In view of the significant variability of measurements and the bias of chloride ingress model, it remains unaddressed how to formulate a measurement plan to make the calibrated model achieve the required accuracy. This paper first establishes the probabilistic time-dependent models of CS and DCl with both sample variance and model bias considered, and then introduces the Bayesian method to update the two models using measurement data. By assuming realistic models of CS and DCl and comparing them with updated ones, the effectiveness of Bayesian updating method is demonstrated, and the key factors affecting the updated model accuracy are discussed, including prior estimate of parameters, model bias and measuring times. On this basis, a determination method of measurement plan targeting the calibrated model accuracy is proposed, which works for both Bayesian updating and linear fitting for model calibration. And finally numerical examples are presented to show the validity of the proposed method. The sample size obtained by the proposed method is exact for linear fitting and slightly more than required for Bayesian updating.

Structural resistance deterioration is by nature a non-increasing stochastic process with autocorrelation on the temporal scale. The Gamma process is often used to describe the stochastic behavior of resistance deterioration. With Gamma-based deterioration models, the calculation of time-dependent reliability presents a serious challenge, and often Monte Carlo simulation is the only solution to this problem. This paper derives a closed-form solution for time-dependent reliability of aging structures, in which the resistance deterioration is described by a Gamma process and the applied load is modelled as a Gaussian process with a constant standard deviation. The accuracy of the proposed method is verified through a comparison with Monte Carlo simulation results, and its applicability is further illustrated in a time-dependent reliability analysis of an existing highway bridge whose vehicle load is modelled using weigh-in-motion (WIM) data. It is found that the measured vehicle load has a relatively small uncertainty, and the uncertainty associated with resistance deterioration is crucial to the reliability assessment because it dominates the overall uncertainty in the reliability calculation.

Reliability updating can be interpreted by the process of reevaluating structural reliability with data stemming from structural health monitoring sensors or platforms. In virtue of the power of Bayesian statistics, reliability updating incorporates the up-to-date information within the framework of uncertainty quantification, which facilitates more reasonable and strategic decision-making. However, the associated computational cost for quantifying uncertainty can be also increasingly challenging due to the iterative simulation of sophisticated models (e.g., Finite Element Model). To expedite reliability updating with complex models, reliability updating with surrogate model has been proposed to overcome aforementioned limitations. However, the past work merely integrates reliability updating with Kriging-based crude Monte Carlo Simulation, thereby, still exists many computational limitations. For example, parameters such as the coefficient of variation of posterior failure probability, the batch size of samples, and active learning stopping criterion are not well defined or devised, which can lead to computational pitfalls. Therefore, this paper proposes RUAK-IS (Reliability Updating with Adaptive Kriging using Importance Sampling) to address the aforementioned limitations. Specifically, importance sampling is incorporated with Kriging to enable updating of small failure probability with robust estimate and error quantification. Two numerical and one practical finite element examples are investigated to explore the computational efficiency and accuracy of the proposed method. Results demonstrate the computational superiority of RUAK-IS in terms of robustness and accuracy.