Damage State Research Articles

Hybrid simulation testing of two-storey low-aspect-ratio nuclear RC shear walls with normal- and high-strength reinforcement: Seismic performance evaluation and economic assessment

Low-aspect-ratio reinforced concrete (RC) shear walls have been commonly used in several nuclear facilities in containment and safety-related structures. Despite being a potential alternative to reduce rebar congestion and subsequently minimize complex construction activities typically associated with nuclear facilities, there has been limited experimental research on investigating the impact of using high-strength reinforcement (HSR) on the seismic performance of such walls, particularly in a multi-storey context. This lack of research is mainly due to considerable challenges imposed when testing such multi-storey nuclear RC shear walls in most laboratories. Therefore, the current study presents the experimental results of two two-storey low-aspect-ratio nuclear RC shear walls that were tested utilizing the seismic hybrid simulation testing technique. In this respect, walls W1-NSR and W2-HSR were designed using normal-strength reinforcement (NSR) and HSR, respectively, where the two test walls had comparable capacities to allow for direct comparisons. Both walls were subjected to various ground motion levels, spanning from operational to design and beyond-design earthquake scenarios. The experimental findings are then presented to include the force-displacement responses, the multi-storey effects, ductility capacities, lateral and rotational stiffnesses, rebar strains, and cracking patterns of the test walls. Subsequently, an economic assessment was carried out to quantify the total rebar weights and the corresponding construction costs of such walls. In addition, the expected seismic repair costs were determined based on a three-dimensional digital image correlation technique that provided information on the damage states of the test walls under different earthquake levels. The results show that although W1-NSR and W2-HSR attained similar force and moment capacities, W2-HSR achieved a relatively higher ductility capacity than W1-NSR. However, larger cracks were observed in W2-HSR compared to W1-NSR, which was attributed to the associated larger rebar spacing in the former relative to the latter. The economic assessment results demonstrate that using HSR minimized the rebar weights and construction costs, while both walls had similar seismic repair costs at their design and beyond-design earthquake levels. Both the seismic performance and economic assessment results presented in the current study are expected to aid future editions of relevant design standards in adopting HSR in nuclear construction practice.

Seismic fragility analysis of long-span rigid-frame bridge on mountainous soft clay site

In order to assess the damage condition of bridge components for a large-span rigid bridge in a soft clay site in a mountainous area in China southwest, a finite element model of a large-span rigid bridge is established based on the OpenSees software, and the joint probability density distribution function of the ground motion strength and seismic demand and the marginal distribution function of the ground motion are introduced into the kernel density function. As a basis to get the method of calculating the fragility of the bridge members, and the method is verified for its feasibility, on this basis, the damage condition of the bridge components are analyzed, and finally the damage condition of the bridge system are analyzed by the first-order bounds method and the improved PCM method (IPCM). The results showed that: (1) Kernel density method (KDE) can effectively calculate the damage probability of each component, for example, under ground motions with PGA equal to 0.2 g, the probability of slight damage of the 1# pier is 29%, that of the intermediate consolidation pier (2# pier ~ 4# pier) is about 90%; the probability of slight damage of the 1# bearing is 48%, and that of the 2# bearing is 87%. (2) Reasonable value of the expansion joints can effectively reduce the probability of main beam collision. In this investigation, the value is taken as 0.18 m ~ 0.24 m. (3) The bridge system is more likely to be damaged than a single component in the system, and the damage probability of a single component cannot be used as a criterion for the bridge system in the actual working condition. Comparing the first-order boundary law with the IPCM method, the IPCM method has higher accuracy.

The 3D plan analysis under progressive collapse for RC buildings through demand capacity ratio (DC) for three levels of damage

The progressive collapse presents a sudden load may cause initial failure in local parts at the frame which could lead to sequence reactions in the elements of the structure. A building of seven storeys was chosen to designed under gravity load according ACI 318 Code. This building is analyzed under progressive collapse condition for two columns damage cases; edge damage, corner damage. The building needs to be loaded as specified according (GSA) General Services Administration requirements through linear static analysis. The results of linear analysis show the variation in ((DC)) demand capacity ratio for the columns. The aspects of damage action in this study regarding section size and damage cases with 100% damage column loss (full damage), 60% damage (0.6 section size), 80% damage (0.8 section size). A GSA guideline to typical building with (DC) values bigger than 1.5 refer to critical potential damage conditions at the columns of the frame. It can be seen of the results the effect of size reduction for the building. This effect is clear to be decreased along distance from any plan. The((DC)) for columns which exceeded 1.5 value which show risky damage condition could actually happened that present serious threat possibility specially for nominated columns C22, C29 and C23 at plans A and B which show middle columns at lower position of the building.

A novel semi-convex function for simultaneous identification of moving vehicle loads and bridge damage

For the simultaneous identification (SI) problem on the moving vehicle loads and bridge damage, the existing methods fail to appropriately consider the sparse characteristics of structural damage and ignore the prior information of vehicle loads, which make them suffering from challenges such as prolonged computation cost, poor noise robustness, and limited identification accuracy. To address this issue, a novel semi-convex function is proposed to construct a theoretical framework for the SI problem using the bridge dynamic responses subjected to moving vehicles in this study. Firstly, the bridge damage-induced virtual forces are conceptualized as a new form of moving residual forces characterized by block sparsity properties, and the damage location is then determined by the frequency characteristics with the largest fundamental frequency amplitude of moving residual forces. Secondly, a semi-convex function model encompassing moving forces and moving residual forces is defined. Combined with sparse regularization strategy, an improved alternating direction method of multipliers algorithm is proposed and applied to simultaneously solve for both moving vehicle loads and damage degrees. To assess the effectiveness and feasibility of the proposed method, extensive numerical validations are conducted in various bridge damage scenarios. Additionally, a series of truss bridge experiments are performed in laboratory under multiple damage and vehicle axle-weight conditions. The results show that compared with the existing outstanding methods, the proposed semi-convex function method can significantly reduce the identification cost, enhance robustness to noise, and demonstrate notable improvements in accuracy for the SI problem, which provides an innovative and more efficient solution to the SI problem in bridge engineering.

Biomonitoring of hand and forearm transepidermal water loss and skin PH among nursing apprentices.

Measurements of transepidermal water loss (TEWL) and stratum corneum (SC) pH can help indicate work-related skin barrier damage, but sensitivity to confounding personal and ambient factors limits their potential as biomonitoring tools. To evaluate the difference between hand and forearm skin barrier conditions as a tool for early recognition of workers with occupational contact dermatitis. The participants were nursing apprentices (N = 238, median age 19 years) from Zagreb, Croatia. They filled out a questionnaire based on the Nordic Occupational Skin Questionnaire, underwent a clinical examination of skin on the hands, and were evaluated for their TEWL and SC pH on the dorsum of the hand and volar part of the forearm. We found that the difference between hand and forearm TEWL values (ΔTEWL) greater than 7 g/m2/h, or >50%, or the difference in SC ΔpH >0.50, predicted visible skin changes found on clinical examination. However, only the association with ΔpH >0.50 retained statistical significance when controlled for sex, age, ambient temperature, and relative humidity in a multiple regression model. The difference between hand and forearm SC pH values is suggested as a reliable biomonitoring tool in recognition of damaged skin barrier conditions in occupational settings.

Impact of freeze recovery method on high-speed fracture in metal cylindrical shells

The freeze recovery method (FRM) is a crucial approach for investigating the high-speed fracture process of metal cylindrical shells under explosive loading. However, the precise impacts of the recovery process on the deformation and fracture behavior of such shells remain unclear, significantly constraining the widespread application of this method in high-speed fracture studies. This paper quantitatively evaluates the effects of the expansion contour, fracture mode, and damage state of intermediate shells at different stages of fracture development using a crack evolution simulation method and nondestructive crack detection technique. The recovered shell contour can effectively represent the free expansion contour of the shell at the equivalent moment, with an error of less than 4%. Impact induces tensile cracks on the outer wall of the shell, which leads to changes in the local fracture mode. A method of crack elimination and equivalence in the damage statistics of the recovered shell is proposed to address this effect. The recovered shell can characterize the damage evolution during free expansion at the equivalent moment after eliminating the influence of excess tensile cracks. Based on the principle of stress analysis and energy conservation, the formation mechanism of tensile cracks in the outer wall of the shell is explored, and the correlation between tensile cracks and recovery time is elucidated. The study shows that the impact of fracture damage caused by freezing recovery is gradually reduced over time. The improved freezing recovery method based on the hard recovery principle is successfully used to recover the multistage intermediate shell, meeting the demand for obtaining the transient physical model in the high-speed fracture field.

Technologies and Platforms for Remote and Autonomous Bridge Inspection – Review

Recent scientific and technological advancements have enabled a more efficient structural condition assessment of bridges, mainly through the implementation of intelligent inspection strategies. These intelligent strategies can provide early identification of critically damaged components before failure, and will therefore play a key role in extending the life of infrastructure. The latest inspection technologies can provide inspection plans with damage conditions, create a damage report as well as provide statistics and comparisons to the previous inspection findings. The new and existing inspection technologies are directed to help with the digitalization of the Bridge Management System (BMS). The complexity of maintenance/inspection requires organized, automated, open and transparent digital processes, which should consider both—structure and asset management data. Further, the inspection/monitoring findings serve as a source for decision-making models. The digitalized aspects of autonomous inspection provide better performance prediction models and guarantee safety for the users. This paper presents the latest findings in the field of remote inspection of bridges. In particular, the main technologies for inspection and geometrical assessment are depicted, especially those based on computer vision systems installed in UAVs and robots, LiDAR, radar, satellites and other non-contact systems including on-board monitoring. The accompanying article entitled: “Methodologies for remote bridge inspection” deals with the methodologies used for data processing based on Artificial Intelligence (AI).

A Bayesian network-based probabilistic framework for seismic vulnerability assessment of road networks

After an earthquake, roadside debris from collapsed buildings, structural damage to bridges, and large-scale emergency evacuations are major obstructions to the connectivity of road networks (RNs). However, few studies have comprehensively examined the impact of all these obstructions on the seismic vulnerability of RNs while considering uncertainties. In this paper, a novel probabilistic framework is proposed for seismic vulnerability assessment of RNs based on Bayesian networks (BNs). Herein, the vulnerability of RNs is defined as a combination of the failure probability of a road link and its conditional consequences in terms of network dis-connectivity. The framework can be used to assess the effects of debris distributions, the functionality losses of bridges, and evacuation flows on the failure probabilities of road links and the seismic vulnerability of RNs. Uncertainties in the post-earthquake damage states of buildings and bridges, building collapse types, building-to-road distances, and travel costs are explicitly considered and propagated in the framework using Monte Carlo (MC) simulations. The MC results are used to establish prior probabilities, and based on bidirectional inference with BNs, it is possible to assess the seismic vulnerability of RNs and identify critical roads. The proposed methodology is verified through a real-world RN in China.

Investigating the simulation test and acoustic emission characteristics of structural-control type rockbursts in deep underground environments

Deep underground projects are in a complex in-situ stress environment, and rock burst disasters induced by the hard and brittle failure of the rock mass are prone to occur around the tunnel openings. In this study, we aim to investigate the impacts of geological weak surfaces in rock masses on rockbursts and the spatiotemporal evolution of microcracks during the development of rockbursts. To achieve this objective, two sets of cubic specimens containing a circular through-hole were designed. One set of specimens had prefabricated unfilled cracks around the openings. Subsequently, the rockburst development process within the deep tunnel was replicated by subjecting both groups of specimens to identical gradient loading using a true triaxial test system. During the test, a micro camera was used to record the failure of the borehole wall in real-time, and an acoustic emission monitoring system was utilized to capture the stress waves released during structural damage. Finally, a multi-level synergistic analysis of the rockburst damage mechanism was carried out based on the macro-imaging data and micro-acoustic emission data. The results show that failure in high-stress environments within tunnels mainly includes microcrack initiation, particle ejection, crack propagation, local cracking, slabbing spalling, damage penetration, and rockburst damage. The time–frequency domain information of acoustic emission signals is highly perceptive and representative of the structural damage state of the specimens. The mutation characteristics observed in time-domain parameters, such as acoustic emission amplitude, event rate, ringing, and cumulative energy, correspond to drastic alterations in the internal structure of the rock. The distribution characteristics of frequency-domain parameters, such as acoustic emission peak frequency, dominant frequency energy, and frequency centroid, reflect different crack scales and damage modes. The diffusion of frequency centroid indicates that the damage and failure patterns within the rock are evolving towards a more complex direction. The energy magnitude of acoustic emissions represents the damage intensity during the development of rockburst. The root causes of induced structural-control type rockbursts in deep hard brittle rock masses are the combined effects of localized high-stress concentrations and large-scale discontinuous geological structures, such as natural joints, fissures, and structural planes. The naturally occurring large-scale through-type geological weak surfaces within the rock mass reduce the strength of the surrounding rock, alter the location of stress concentration, and change the initial damage characteristics. Moreover, they promote the evolution of rockbursts, thereby increasing their destructive intensity.

Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings

Compressed sensing (CS) is a promising tool for data compression reconstruction. However, fault diagnosis methods for high-speed train bearings based on CS and acoustic emission (AE) technologies have not been reported yet. Notably, the accuracy and speed of CS two-stage reconstruction methods are affected and restricted by prior initial conditions. Therefore, this article proposes adaptive dynamic thresholds applicable to adaptive stepsize forward–backward pursuit (ASFBP), and bearing health state assessment method. First, the adaptive dynamic thresholds for atom selection and deletion are constructed based on the residual feedback mechanism and the atom quality distribution law, which enables ASFBP to realize high-precision rapid reconstruction of signal without any atom priori initial conditions. Second, the initial dictionary length is improved based on the AE hit characteristics. Furthermore, a damage state comprehensive evaluation index (DSCEI) is established using principal component analysis based on AE time-domain hit parameters and compression-domain energy parameter. Compared with the kurtosis index and permutation entropy index, the DSCEI demonstrates better monotonicity and stability in the quantitative evaluation of high-speed train bearing condition. Finally, the validity and stability of the method are verified by testing under complex test conditions resembling actual high-speed train lines, providing valuable insights for the CS-based data-driven bearing fault diagnosis.

Modified fragility functions for offshore wind turbines considering soil-structure interaction subjected to wind, wave, and seismic loads

Investment allocation for offshore wind turbines (OWT) as an important class of structures is typically carried out through supporting decision-making approaches utilizing some fragility functions. This study attempts to deliver fragility functions for OWTs on monopile foundations accounting for soil-structure interaction (SSI) effects. Simultaneous wind, wave, and earthquake loads were considered probabilistically by adjusting their occurrence hazard levels for predefined damage states in diverse performance levels. The designated damage states in this study are defined based on collapse probability and some targeted performance levels which could be very straightforward to distinguish. The damage state detection is based on rotation in the connection section of the tower’s transition part to the foundation, which perceptibly reveals the effects of SSI on fragility functions. The expected results comprise modified fragility functions accounting for SSI effects contributing to less median spectral acceleration, more evidently rotational demands, further dispersions, and a subsequent dominant increase in the probability of exceeding performance limit states. Considering operational performance level, the most applied design performance level for turbines as an important class of structures, not considering the SSI effects could noticeably underestimate the demands and lead to high-risk decisions.

Seismic response and fragility analysis of railway station typical signal cabinets

The loss of functionality in railway stations due to signal cabinet damage is frequently reported after earthquakes. To maintain the post-earthquake functionality of railway stations, it is essential to study the seismic response and fragility of signal cabinets. This paper divided the structure of railway station signal cabinets into combined cabinets and nine-fold cabinets based on previous investigations. The frequency-shift and train-control cabinets were selected as typical research objects representing these two types. Shaking table tests were conducted to examine their damage characteristics, seismic responses, and failure mechanisms. Based on these tests, finite element models of the two typical cabinets were established and verified, which were then used to analyze cabinet layering and weight distribution in practical applications. Moreover, seismic fragility curves were developed for frequency-shift and train-control cabinets under various damage states, utilizing both test and finite element results. These curves can be applied in seismic performance and functionality assessments of railway stations.

Research and Modification on the Park-Ang Damage Index for Railway Rectangular Piers

ABSTRACT This study addressed the issue of distortion in evaluating the seismic damage state of railway bridge piers using the original Park-Ang damage index. By constructing a parameterized co-simulation program with two finite element models, OpenSees and Abaqus, five sets of flexural failure and five sets of flexural-shear failure rectangular pier quasi-static test results were selected to validate the rationality of the calculation program. Four hundred and eighty parameterized calculations were conducted using this program. The combination coefficient β and critical damage index DI within the Park-Ang damage index were modified using nonlinear regression algorithm and convolutional neural network algorithm. The adaptability of the damage index calculation was ultimately verified through an engineering case study of a simply supported beam bridge. The research findings are as follows: 1) The main factor influencing β is the longitudinal reinforcement ratio, while the axial compression ratio, shear span ratio, volumetric stirrup ratio, aspect ratio, concrete strength, and steel strength are secondary parameters influencing β. 2) Compared to the nonlinear regression algorithm, the convolutional neural network algorithm can better establish a calculation model between pier structural parameters and β, clarifying their relationship. 3) It is recommended to use critical values of 0.11, 0.29, 0.49, and 1.00 for assessing pier damage as no, slight, moderate, severe, and collapse damage when using the damage index calculation model. 4) The Park-Ang index corrected by convolutional neural networks demonstrates improved accuracy and computational stability, making it suitable for practical assessment of seismic damage in bridge structures.

Seismic Resilience Assessment for Steel-concrete Composite Bridges including Impacts of Near-fault Earthquakes

This paper proposes a seismic resilience assessment method for steel-concrete composite bridges (SCCB) considering near-fault earthquake hazards. Based on conventional probabilistic seismic hazard disaggregation analysis, a correction factor is defined to represent the proportion of the occurrence probability of the near-fault pulse-like, near-fault non-pulse-like and far-field earthquake conditioned on a given intensity level concerning the total occurrence probability of all earthquakes. The parameters of functionality recovery functions are modified using the factor proposed, and then the restoration processes after each type of earthquake are estimated. Correspondingly, vulnerabilities of a typical SCCB under near-fault and far-field earthquakes are developed as a case study. Based on the seismic hazard and fragility results, the seismic risk for each type of earthquake in a 50-year horizon is estimated. After that, the modified functionality recovery function is derived from the expected functionality. For implementation of the method proposed, the expected seismic resilience indices of a typical SCCB involved in the SEQBRI project are estimated, and the seismic resilience assessment is conducted. For comparison analysis, the seismic resilience assessment without considering earthquake type is also conducted using the same bridge. The result shows that the seismic resilience of bridges in near-fault earthquake scenarios can be analyzed by the method proposed, and reducing the structural vulnerability under low-intensity level earthquakes and improving the structural recovery efficiency for slight and moderate damage states are more meaningful to enhance the seismic resilience of bridges.

A New Methodology for the Diagnosis and Monitoring of Bridges Under Slow Deformation Phenomena

Roadway bridges can be subjected to several pathologies affecting their structural performance during operational conditions. Among others, slow deformation phenomena due to landslides often represent the primary cause of the onset of damage and collapse mechanisms. The timely identification of slow deformation phenomena on bridges and viaducts, as well as the evaluation of their extent and evolution over time, are therefore of crucial importance for preserving the safety conditions of road networks. In common practice, the preliminary analysis of slow deformation phenomena could be carried out over large geographical areas, hence used by managing institutions to set intervention priorities among the assets under their responsibility. In this regard, this paper proposes a new methodology for the diagnosis and monitoring of bridges under the effects of slow deformation phenomena based on the combined use of Synthetic Aperture Radar Interferometry (InSAR) techniques, visual inspections, geometric surveys, destructive and non-destructive testing methods, and numerical analyses. Specifically, InSAR is adopted to remotely identify bridges affected by slow deformation phenomena within large geographical areas/road networks. Then, visual inspections are carried out on the identified structures to investigate their defectiveness and the detected deformation phenomenon. Depending on the evaluated damage state, a bridge may be subjected to continuous InSAR monitoring, thus enabling the systematic analysis of the evolution of the deformation phenomenon and relevant anomalies over time, or an accurate structural assessment via numerical analyses. In this last circumstance, information gathered from geometric surveys and field tests can be used for the fine-tuning of the numerical models of the structures. A practical application of the proposed methodology for the diagnosis and monitoring of a curved roadway bridge subjected to landslide phenomena is also presented in the paper for illustrative purposes. Overall, the results obtained in the case study provide a sound demonstration of the effectiveness of the proposed approach.

A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water-Rock Interaction.

The mechanical properties of fissured sandstone will deteriorate under water-rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water-rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF), b value, and the natural/saturated state of fissured sandstone: state. To enhance data utilization, a 10-fold cross-validation method was employed during the model training process. The machine learning models were developed and designed to identify the instability risk of fissured sandstone under the natural and saturated states. The results demonstrated that the established RF model was capable of identifying fissured sandstone instability risks with an accuracy of 97.87%. Feature importance analysis revealed that state and b value exerted the most significant influence on identification results. The Spearman correlation coefficient was utilized to assess the correlation between input features. This study can provide technical support to identify the risk of instability of fissured sandstones under both natural and saturated water conditions. Based on the models developed in this study, it is possible to implement an early warning method for instability in fissured sandstone that meets realistic working conditions. Compared with the traditional empirical and formulaic methods, the machine learning method can more quickly process huge amounts of AE data and accurately identify the damage state of fissured sandstone.

Seismic vulnerability assessment of urban areas made of adobe buildings through analytical and numerical methods: The case study of the historical center of Cusco (Peru)

This paper aims at giving a contribution to the topic concerning the preservation of valuable adobe buildings, by setting a methodology for assessing their seismic vulnerability at the urban scale by means of analytical and numerical models, accounting for both their in-plane and out-of-plane behaviors. To this purpose, the valuable historical center of Cusco (Peru) is considered as representative case study. The city center of Cusco is an extraordinary example of colonial architecture that preserves essential aspects of its origins, it being representative of a large plethora of historical centers in South America and the Caribbean, where most of the residential buildings are made of adobe masonry.First, archetypes, representative of the whole historical center, are identified in order to get a manageable stock of buildings for which the analyses can be addressed to. The structural capacity of each archetype is then given in terms of capacity curves for both out-of-plane and in-plane mechanisms. Subsequently, these curves are compared with the likely seismic demands to assess the probability of attaining pre-established damage states for different seismic intensities. The obtained probabilities are collected into Damage Probability Matrices (DPMs) and, finally, cumulated to plot the fitting fragility curves.

Combining transfer learning and numerical modelling to deal with the lack of training data in data-based SHM

Structural health monitoring (SHM) involves continuously surveilling the performance of structures to identify progressive damage or deterioration that might evolve over time. Recently, machine learning (ML) algorithms have been successfully employed in various SHM applications, including damage detection. However, supervised ML algorithms often require labelled data for multiple possible damage states of the structure for successful damage identification. Although it may be feasible to gather such data for low-value structures, obtaining damage data for expensive structures such as aircraft could be highly challenging. Herein, this data insufficiency is addressed by combining Finite Element (FE) models with domain adaptation, specifically transfer component analysis (TCA) and joint domain adaptation (JDA). The proposed methodology is showcased in two case studies, a Brake–Reuß beam, where damage scenarios correspond to different torque settings on a lap joint and a wingbox laboratory structure where damage is introduced as saw-cuts. Supervised learning algorithms in the form of Artificial Neural Networks (ANNs) and K-Nearest Neighbours (KNNs) are trained based on FE data after domain adaptation is applied and are then tested with the experimental data. It is shown that even though the performance of classifiers in distinct scenarios of dual, three, four and five-class cases is sensitive to choices in the training stage, the use of TCA or JDA allows for the use of FE data for training and significantly reduces the need for expensive experimental damage data to be used for training. These results can pave the way for a broader use of ML algorithms in SHM of critical and/or expensive structures.

Serviceability fragility assessment of non-seismically designed railway viaducts in Turkey under near-field and far-field earthquakes

While many railway viaducts still in use in Turkish railway networks are located on active fault zones that produced historical destructive earthquakes, they are not seismically designed in accordance to current standards. This study aims to provide a better understanding about the serviceability fragility of such systems by conducting detailed probability-based seismic assessments under near-field and far-field earthquakes. To achieve this, firstly, 3D finite element models of a range of selected viaducts in Turkey were generated based on their original design drawings. The developed FE models were validated by comparing the analytical modal frequencies with the results of in-situ dynamic tests involving a series of acceleration measurements. Nonlinear time history analyses were then carried out under 25 near-field and 25 far-field real three-component ground motion recordings to obtain the seismic response of each selected viaduct. Subsequently, probabilistic seismic demand models were defined using linear regression analysis to determine relationships between engineering demand parameters (EDPs) and ground motion intensity measures (IMs). Peak ground acceleration (PGA), peak ground velocity (PGV) and spectral acceleration at 1.0 s (Sa (1.0)) were used as the IM parameter, while the maximum lateral displacement of the bridge spans for different service velocities defined in the Eurocode was considered as the EDP at serviceability damage state. Finally, analytical fragility curves for all the selected railway viaducts were developed considering maximum damage probability for the IM levels. The results, in general, demonstrate the seismic vulnerability of the existing viaducts in Turkey. It is shown that while at low speed limits the viaducts exposed to far-field ground motions were more vulnerable than those under near-field ground motions, at high speed limits the viaducts subjected to near-field ground motions were more vulnerable. Also, it is seen that reinforced concrete and masonry viaducts are generally more vulnerable to earthquakes than the steel viaducts. The outcomes of this study should prove useful for the seismic risk assessment, loss estimation and rehabilitation of the railway transportation networks in future studies.

Experimental investigation on damage development and failure mechanism of shield tunnel lining under internal blast considering stratum-structure interaction

With the expansion of international terrorism and the potential threat of attacks against civil infrastructure, the dynamic response and failure modes of underground tunnels under explosive loads have become a prominent research topic. The high cost and inherent danger associated with explosion experiments have limited current research on tunnel internal explosions, particularly in the context of scaled model tests of shield tunnels. This study presents a series of scaled model tests under 1 g-condition simulating internal blast events within a shield tunnel based on the prototype of the Shantou Bay Tunnel, considering the influences of surrounding stratum and equivalent explosive yield. Three different TNT explosive yields are considered in the model tests, namely 0.2, 0.4, and 1.0 kg. The model tests focus on the damage behavior and collapse modes of the shield tunnel lining under internal explosive loads. The model tests reveal that the shield tunnel is prone to damage at the joints of the tunnel crown and shoulder when subjected to internal explosive loads, with the upper half of the tunnel lining experiencing segment collapse, while the lower half remains largely undamaged. As the TNT equivalent increases, the damage area at the tunnel joints expands, and the number of segment failures in the upper half of the tunnel rises, transitioning from a damaged state to a collapsed state. The influence of “stratum-structure” interaction is investigated by comparing two models, one with overburden soil and the other positioned at the ground surface. The model tests reveal that the presence of soil pressure and confinement can significantly enhance the tunnel resistance to internal blast loads. Based on the observation of the model tests, five different damage modes of segment joints under internal explosion are proposed in this study.