Structural Safety Research Articles

Design and Analysis of Curved Steel Bridges Using 3D Shell Modelling

AbstractSteel bridge designers and erectors primarily rely on approximate methods, such as 1D line girder analyses and 2D grid analyses. For curved or skewed bridges, this can give erroneous indications of the structure's true behaviour. Refined methods of analysis, such as 3D shell modelling, aim to bring a significant change in the practice of bridge engineering. By moving the industry away from simplistic design assumptions, refined analysis methods lead to more accurate, less conservative results, which in turn brings true benefits such as improved structural safety and increased economy. In this paper, several case studies on curved plate girder and tub girder bridges are presented, which cover the full bridge life cycle, from erection of the steel structure and placement of the concrete deck, down to live load analyses using influence surfaces and load rating. The case studies will be modelled with mBrace3D, a dedicated FEA software for curved steel bridges.

Active Control for Construction Stability of Suspension Arch Rib Segments Based on ARID System

In response to the issues of vibrations affecting the construction safety, speed, and accuracy of suspended structures due to dynamic disturbances during lifting, environmental disturbances, and improper operator actions, this paper investigates the application of the Active Rotary Inertia Driver (ARID) system for double-rope suspended structures, namely analyzes, and applies control to the vibration response of double-rope suspended structures under external excitations. Taking the double-rope suspended structure as an example, a dynamic analytical model of the double-rope suspended structure is established based on D’Alembert’s principle. Through shake table tests, the dynamic response characteristics and vibration control effects of the double-rope suspended structure under various external excitations are studied, and the influence of the twisting radius of the suspended structure on the control effect is analyzed. The experimental results show that the ARID control system achieves significant control effects on the swing and torsional vibrations of the suspended structure, which validates the accuracy of the theoretical model for the ARID control system and demonstrates its feasibility for practical applications.

Meso-crack propagation of RC induced by non-uniform rebar corrosion and the predictive model of time to concrete cover cracking

Non-uniform corrosion expansion in reinforced concrete (RC) structures will lead to concrete cracking, bond degradation, and an overall reduction in load-bearing capacity, severely threatening structural safety and durability. Accordingly, meso-crack propagation of RC induced by non-uniform rebar corrosion and the predictive model of time to concrete cover cracking are investigated in this paper, and the theoretical solutions for the critical corrosion rate and the corresponding time are proposed for structural durability evaluation and design. Firstly, based on the mesh-mapping technique of multi-shape random aggregates, a finite-element generation method for three-dimensional concrete three-phase meso-scale model is proposed, with modeling corrosion-expanded cracking of RC, reproducing the crack morphology and propagation process of concrete cracking due to non-uniform rebar corrosion. Subsequently, single factor influence analysis and multi-factor orthogonal test were employed to reveal the degrees of influence of various factors on the critical corrosion rate as follows: concrete strength > cover thickness > interface layer strength > aggregate content > aggregate grading > aggregate shape. A theoretical predictive model for the corrosion expansion cracking time of the RC cover was proposed, considering the influence of key factors including concrete strength and cover thickness. Finally, the validity of theoretical prediction method and the numerical model’s damage mode are verified through accelerated corrosion tests of RC.

Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach.

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model's decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice.

Evolution of structural safety for tunnel arch crown linings under the coupled influence of void and insufficient thickness

The existence of voids behind tunnel linings constitutes a recognized inherent defect in tunnel engineering. Void and insufficient thickness can significantly compromise structural safety, especially at the tunnel arch crown. In this study, we employed similar model tests and numerical analysis methods to examine how the coupling of voids and insufficient thickness affects the structural performance of arch crown linings. Key findings reveal that the coupling of arch crown voids and insufficient thickness causes notable shifts in displacement patterns and internal force distribution. The displacement pattern of the arch shifts from the inner side to the outer side of the tunnel, resulting in the concrete stress state on the inner side of the arch crown changing to compression. Spatial distribution characteristics of voids and insufficient thickness defects were analyzed to assess their impact on structural degradation, with quantitative analysis using the safety factor index. Geometric parameters of voids and insufficient thickness (longitudinal length, thickness, circumferential range) were chosen based on analysis under various conditions to predict the structural safety factor using a Wide Neural Network regression model with 98 % accuracy. This method offers a new approach for rapidly predicting the safety status of lining structures. These findings are crucial for comprehending the effects of arch crown voids and insufficient thickness defects and for devising effective strategies to address tunnel lining defects.

Experimental study on low-cycle fatigue performance of high-strength bolted shear connections

In strong earthquakes, low-cycle fatigue fractures may occur in the bolted connections of steel braced frames. Ensuring a high low-cycle fatigue life for high-strength bolted connections is of paramount significance in enhancing the overall safety and collapse resistance of steel structures. Despite substantial research on plate fatigue failure within bolted connections, there remains a research gap regarding low-cycle fatigue failure of high-strength bolts in these connections. This study aims to address the aforementioned shortcomings by conducting 34 sets of low-cycle fatigue tests on high-strength bolted connections. The test specimens all experienced low-cycle shear fatigue fracture at the threaded or unthreaded portion of the bolt shank as the dominant failure mode. Residual deformations were observed in the holes of connection plates. Based on the test data, ultimate shear capacity of a single shear connection is around 0.61Fy (tensile yield bearing capacity of bolt) when the shear force passes through the unthreaded portion and drops to 0.54Fy for the shear force at the thread. The untreated Q355 (minimum yield strength of 355 MPa) steel surface has an average anti-slip coefficient of around 0.37, while milling reduces it to approximately 0.30. The influence patterns of loading amplitude, bolt material, minimum plate thickness, bolt diameter, shear force position and connection type on low-cycle fatigue life are given by range analysis. Among them, variance analysis shows that loading amplitude and bolt material are the primary influencing factors. The correlation coefficient between loading amplitude and low-cycle fatigue life is −0.92, indicating a strong negative correlation. The degradation amount and rate of anti-slip capacity and shear capacity of bolted connections are also investigated. To propose reliable life prediction method, three models were used to establish the relationship between dimensionless shear deformation and low-cycle fatigue life of 10.9-grade high-strength bolted connections. It is not appropriate to use the fatigue categories defined in current standards to predict the low-cycle shear fatigue life of high-strength bolted connections.

Transfer-AE: A novel autoencoder-based impact detection model for structural digital twin

Accurately detecting the location and intensity of impacts is crucial for ensuring structural safety. Currently, AI-based structural impact detection methods are widely used for their excellent detection accuracy. However, their generalization capability is limited by the scenarios present in the training data. Many complex and dangerous impact scenarios are difficult to conduct real-world experiments on to collect sufficient samples. To capture all impact scenarios and fully leverage the advantages of AI-based detection technologies, advanced methods involve combining real-world structural monitoring data with corresponding numerical models to construct digital twins. These methods continuously refine the created numerical models with limited real-world data and provide diverse impact scenarios through numerical model simulations. However, there are inevitable differences between digital models and physical models that are challenging to correct through mechanical means. This discrepancy in data distribution between the two models significantly hinders the application of digital twin technology in impact/event identification tasks. To address this challenge, this study proposes a novel model based on autoencoders, named Transfer-AE. Transfer-AE encodes the common features of digital twins in the latent space to bridge the uncertainty gap at a macro scale between numerical models and physical models and synchronously fits the magnitude and location of the impact load in the decoder. This enables consistent detection results for the same impact event, whether the sample comes from the numerical model or the physical model. Transfer-AE includes two operating modes: Mode 1 has a fixed computational complexity with stable inference speed, but the training cost and difficulty increase with data distribution. Mode 2's computational complexity increases with data distribution, but it has a fixed training cost and speed. In both cases involving the geodesic dome structure simulating a deep space habitat and the IASC-ASCE benchmark structure, Transfer-AE demonstrated the best performance in impact localization and quantification tasks compared to mainstream domain-adaptive transfer models.

Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading.

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application.

Performance of differently arranged double-tuned mass dampers for structural seismic response control including soil-structure interaction

Reducing vibrations occurring in structures under environmental loads makes a significant contribution to the development of structural safety. Tuned mass dampers (TMD) are one of the systems commonly used to control structural vibrations. Since TMD is adjusted according to the dominant frequency of the structure, factors that may cause changes in the frequency of the structure can significantly reduce the effectiveness of TMD. One of the main factors that can cause changes in the frequency values of the structure is the interaction between the structure and the soil. To overcome this situation, it is recommended to apply control systems consisting of multiple TMDs (MTMD) to the main structure instead of a single TMD. MTMD systems are arranged in two different types, serial and parallel. In this paper, a methodology that considers the structure-soil interaction (SSI) is presented for the optimum design of control systems with different configurations placed on a main structure under the influence of ground motion. To research the SSI effects, the TMDs-structure coupled system is supported by rocking and swaying springs and the corresponding dashpots, and then the equations of motions of the structural system are derived. In the optimization process based on particle swarm optimization in the frequency domain, different soil types, configuration types, and mass ratios are considered. The efficiency of optimum TMDs arranged in series and parallel is also evaluated using near and far fault ground motions in the time domain. Numerical results show that if structures are constructed on soils with low shear wave velocities, ignoring SSI can significantly change the design parameters and effectiveness of the control system.

Leakage Identification of Underground Structures Using Classification Deep Neural Networks and Transfer Learning.

Water leakage defects often occur in underground structures, leading to accelerated structural aging and threatening structural safety. Leakage identification can detect early diseases of underground structures and provide important guidance for reinforcement and maintenance. Deep learning-based computer vision methods have been rapidly developed and widely used in many fields. However, establishing a deep learning model for underground structure leakage identification usually requires a lot of training data on leakage defects, which is very expensive. To overcome the data shortage, a deep neural network method for leakage identification is developed based on transfer learning in this paper. For comparison, four famous classification models, including VGG16, AlexNet, SqueezeNet, and ResNet18, are constructed. To train the classification models, a transfer learning strategy is developed, and a dataset of underground structure leakage is created. Finally, the classification performance on the leakage dataset of different deep learning models is comparatively studied under different sizes of training data. The results showed that the VGG16, AlexNet, and SqueezeNet models with transfer learning can overall provide higher and more stable classification performance on the leakage dataset than those without transfer learning. The ResNet18 model with transfer learning can overall provide a similar value of classification performance on the leakage dataset than that without transfer learning, but its classification performance is more stable than that without transfer learning. In addition, the SqueezeNet model obtains an overall higher and more stable performance than the comparative models on the leakage dataset for all classification metrics.

Numerical prediction of beam capacity using the new constitutive model based on concrete biaxial behavior

The biaxial strength of concrete exhibits intriguing nonlinearity and scattering, posing challenges to the assessment of building structural safety. Experiments have confirmed that biaxial strength envelopes can manifest concave, quasi-linear, and convex shapes. Despite various empirical formulas being proposed to describe this behavior, they cannot be applied to finite element simulations due to the absence of a constitutive perspective. To study the mechanisms forming the shape of strength envelopes, a novel three-invariant model constitutive model is proposed, which reveals the characteristics of the envelopes resulting from the coupling effect of hydrostatic pressure and Lode angle. By adjusting the model parameters, it effectively describes concave, quasi-linear, and convex envelopes. The model's accuracy and compatibility were validated against experimental data spanning various concrete types, with compression strengths ranging from 19.29 to 96.5 MPa and tension-compression ratios form 0.056 to 0.095. To investigate how biaxial strength impacts the capacity of beams, numerical experiments were conducted on notched and intact beams based on the proposed model. The results underscored that beams prepared using concrete with convex envelopes exhibited higher capacity compared to those with quasi-linear envelopes. Therefore, conducting biaxial tension-compression experiments is recommended to accurately evaluate beam bearing capacity. The proposed model is expected to have further applications in the safety evaluation of building structures.

Aeroelastic Influence of Blade-Shaft Coupling in a 1 1/2-Stage Axial Compressor

Abstract The design process of turbomachinery, is often constraint by aeroelastic phenomena. The design choices are limited by possible structural failure, which can be caused by high vibration amplitudes. In particular, damping has an important impact on these phenomena. In the absence of friction, damping is mainly created by aerodynamics. In this paper, additional damping created by the shaft will be investigated. This becomes relevant when blade vibrations with nodal diameters 1 and -1 couple structurally with shaft vibrations. To investigate the blade-shaft coupling, a simulation process is set up based on a full structural dynamic model of the blisk-shaft assembly and a harmonic balance CFD model to account for the aeroelastic effects. In addition, mistuning identification is performed based on an experimental modal analysis at standstill. All results are incorporated into a structural reduced order model that calculates the vibrational behavior of the blading. These results are compared to damping determined during operation using an acoustic excitation system and measured forced frequency responses. The numerical results agree well with the experimental results, i.e. within the measurement uncertainty. Furthermore, the blade-shaft coupling results in significant changes of the eigenfrequencies and damping. As a consequence, damping increases by up to twelve times due to the coupling. This reduces amplitudes by a factor of nine for the mistuned blade responses. Consequently, higher structural safety factors can be achieved by taking the blade-shaft coupling into account so that the remaining potentials in the aerodynamic design could be better exploited.

Laboratory characterisation of sand-tyre mix as bedding material for buried structures

Bedding material directly interacts with buried structures and supports to sustain external loadings such as traffic and overburden soil pressure. Therefore, soil-structure interaction plays an essential role in the stability and safety of buried structures. In particular focusing on underground pipelines, the annual rate of failures in Australia is around 19 % per 100 km of pipeline and can cost millions of Australian dollars in maintenance. Of these annual failures, ground vibrations from heavy traffic and construction activities are a major cause. In order to reduce such effects of ground vibrations on buried structures, this paper presents the possible application of sand-rubber mixes as a bedding material, where rubber is sourced from recycled tyres. The performance of sand-tyre mixes with regard to the potential for vibration reduction is assessed through this experimental study which comprises of particle size distribution, standard proctor compaction, permeability test, direct shear test, California Bearing Ratio and Repeated Load Test. From these tests, it was found that sand mixed with up to 20 % recycled tyre rubber was the most suitable mix for use as a bedding material.

Study on Strain Field Reconstruction Method of Long-Span Hull Box Girder Based on iFEM

The box girder’s condition significantly impacts the safety and overall performance of the entire ship because it is the primary stress component of the hull construction. This work used experimental research on the long-span hull box girder based on IFEM (Inverse Finite Element Method) technology to ensure the structural safety of the hull box girder. Due to the limitations of conventional experiments in this technical field, such as their reliance on finite element data and lack of input from physical tests, numerous research methods combining the strain sensing data from physical tests with the strain data from virtual sensors were conducted. The strain fields of the top plate, side plate, and bottom plate were each reconstructed in turn, and the verifier measuring points in the physical model test were used to assess the accuracy of the reconstruction results. The findings demonstrate that the top plate, side plate, and bottom plate reconstructions had relative errors of 0.24–7.86%, 0.75–8.13%, and 3.31–2.52%, respectively. This enables the reconstruction of the strain field of the long-span hull box girder using physical test data and promotes the use of iFEM technology in the field of structural health monitoring of large marine structures.

Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data

Flooding and consequential scouring are the primary causes of bridge failures, making the detection of such events crucial for structural safety. This study investigates the characteristics of accelerometer data from bridge pier vibrations and proposes a flood detection method with deep learning-based models based on ResNet18 and 1D Convolution architectures. These models were comprehensively evaluated for (1) detecting vehicles passing on bridges and (2) detecting flood events based on axis-specific accelerometer data under various traffic conditions. Continuous Wavelet Transform (CWT) was employed to convert the accelerometer data into richer time-frequency representations, enhancing the detection of passing vehicles. Notably, when vehicles are passing over bridges, the vertical direction exhibits a magnified and more sustained energy distribution across a wider frequency range. Additionally, under flooding conditions, time-frequency representations from the bridge direction reveal a significant increase in energy intensity and continuity compared with non-flooding conditions. For detection of vehicles passing, ResNet18 outperformed the 1D Convolution model, achieving an accuracy of 97.2% compared with 91.4%. For flood detection without vehicles passing, the two models performed similarly well, with accuracies of 97.3% and 98.3%, respectively. However, in scenarios with vehicles passing, the 1D Convolution model excelled, achieving an accuracy of 98.6%, significantly higher than that of ResNet18 (81.6%). This suggests that high-frequency signals, such as vertical vibrations induced by passing vehicles, are better captured by more complex representations (CWT) and models (e.g., ResNet18), while relatively low-frequency signals, such as longitudinal vibrations caused by flooding, can be effectively captured by simpler 1D Convolution over the original signals. Consequentially, the two model types are deployed in a pipeline where the ResNet18 model is used for classifying whether vehicles are passing the bridge, followed by two 1D Convolution models: one trained for detecting flood events under vehicles-passing conditions and the other trained for detecting flood events under no-vehicles-passing conditions. This hierarchical approach provides a robust framework for real-time monitoring of bridge response to vehicle passing and timely warning of flood events, enhancing the potential to reduce bridge collapses and improve public safety.

Analysis of Fill Dam Using Finite Element Method and Comparison with Monitoring Results

Nowadays, a detailed safety policy is applied for dams. These policies cover structural safety, monitoring, inspection, safe operation, and emergency plans. For high-risk dams, all these policy elements need to be included in dam safety programs. Deficiencies in embankment dams, which suffer the most damage, can be detected by visual inspection and programmed monitoring of dams. In dams, horizontal and vertical deformation, leakage, pressure, stress, loads acting on structural elements, and environmental factors are generally measured. These behaviors can be numerically modeled to determine the dam behavior. Numerical analysis methods are important for monitoring the safety of the dam. Models created with software such as Plaxis provide information about dam behavior. Although numerical analysis is very important for dams, obtaining the material parameters used in the construction of the dam needed for modeling, recording the construction stages of the dam, not taking the water level change in the dam reservoir instantaneously, and not taking the measurement records of the dam measurement instruments correctly for different reasons constitute problems and difficulties for the analyses. Within the scope of this study, İkizdere Dam in Turkey was modeled with the Plaxis finite element program; the survey and piezometer measurement data taken from the dam were evaluated by comparing with the analysis results; the difficulties and problems encountered in the modeling and analysis phase were stated, and recommendations were made on dam safety and numerical analysis. Thus, in addition to other studies, it was emphasized that it is important for dam engineers to monitor the use of numerical analysis models throughout the entire process, not only in the planning phase but also from the planning phase to the life of the dam, and to keep records of all recording intervals that will be needed in digital analysis models.

Physics-informed neural network classification framework for reliability analysis

Reliability analysis is crucial for quantitatively evaluating structural safety amidst uncertainties, laying the foundation for reliability-based design optimization aimed at augmenting structural reliability and reducing economic expenditures, thereby offering substantial engineering benefits. However, performing reliability analysis on intricate structures, especially those requiring time-intensive finite element models, presents a formidable challenge. This study develops an innovative physics-informed neural network classification (PINNC) model to tackle this issue. In the PINNC model, the loss associated with the structural output state (i.e., safety or failed state) is defined as classification loss. Additionally, the structural output value (i.e., actual structural response) is treated as critical physical information, with its associated loss termed as physical loss. To facilitate a separate calculation of physical loss and classification loss, a parametric sigmoid activation function is used, establishing a link between the structural output value and the structural output state. The total loss is calculated as a weighted sum of both physical and classification losses. Unlike conventional neural network classification models that solely focus on classification loss, the PINNC model integrates both physical and classification losses, markedly improving the model’s efficacy in structural reliability analysis. Moreover, an adaptive framework is developed to incrementally include samples proximal to the limit state surface as new training data, thereby reinforcing the PINNC model’s computational precision. The effectiveness of the proposed PINNC framework in overcoming reliability analysis challenges is demonstrated through various applications.

The role of climate change and corrosion modeling strategy in dynamic response of pile-supported wharves

This paper explores the fragility of pile-supported wharves to environmental hazards, notably climate change and corrosion, and underscores the critical need to understand the interplay between these factors when assessing structural safety. The research advocates for comprehensive methodologies that encompass climate change effects, aging, and time-dependent deterioration in evaluating the seismic fragility functions of pile-supported wharves. An examination of aging and seismic effects is performed on a representative pile-supported wharf at designated time intervals. This study highlights the pronounced impacts of climate change and corrosion on the structural integrity of concrete and steel in marine environments. Specifically, it considers effects such as sea level rise, increased temperatures, and heightened relative humidity on pile-supported wharves. Additionally, three corrosion pitting configurations in prestressed strands with and without climate change considerations are analyzed to determine their influence on the strength and ductility of materials, limit states, and ultimately, on the fragility curves. The findings indicate that climate change significantly exacerbates the corrosion of materials in pile-supported wharves, and increases failure probability. The relative increase in corrosion rate after 50 years due to climate change is found to be 94%.

Numerical modelling of global/local mechanisms and sensitivity analysis for the seismic vulnerability assessment of glass curtain walls

Façades represent complex and vulnerable components for buildings, due to the intrinsic fragility and brittleness of glass, and require specific design and calculation tools, both for ordinary and extreme design loads. In this context, the availability of consolidated and strategic modelling strategies is of utmost importance for structural safety considerations and optimal decisions. In this paper, the attention is focused on the assessment of different modelling strategies for the global and local behaviour analysis of Glass Curtain Walls (GCWs) under in-plane seismic loads. Benefits and limitations are discussed for a more refined (but expensive, “MREF”) and a simplified (and computationally efficient, but weak in capturing local phenomena, “MSIMP”) model assemblies developed in ABAQUS and SAP2000, respectively. Careful consideration is given for typical mechanisms, local responses, key performance indicators for the façade components (i.e., stress and deformation peaks) for a reference GCW under monotonic and cyclic loads, as well as for major effects due to general input parameters (i.e., contact mechanisms, clearance, friction, bare frame features, secondary components, etc.), based on a parametric sensitivity investigation. The parametric comparative results are critically discussed, with the support of an experimental prototype test from literature, and the most influencing parameters are emphasized, to improve and facilitate the development of further design and modelling optimization strategies.

Rebar indentation on performance of CFS columns strengthened by rebar at flange-lip

To facilitate easier construction when longitudinally reinforcing cold-formed thin-walled lipped channel steel columns with steel bars, here, both ends of the steel bars used for reinforcement were cut, and the impact on the reinforcement effect was studied through experimental and numerical simulation methods. Seven groups of reinforced column specimens with different indentation lengths and one group of unreinforced specimens were designed, and axial compression tests were conducted. Parametric analyses were performed using the numerical models calibrated by tests. These numerical models included three types of specimens with typical sections and several series of specimens with different steel plate thicknesses. Using these models, the effects of the steel bar indentation length Li, steel bar diameter D, and other factors on the ultimate bearing capacity and deformation of the reinforced specimens were studied. The experimental results indicated that when the steel bar is the same length as the specimen, the compressive bearing capacity of the cold-formed thin-walled lipped channel column can be significantly improved by bonding the steel bar. Additionally, the numerical analysis results indicated that the influence of changing the steel bar diameter D on the bearing capacity of the specimens is not significant when the steel bars are indented within a certain range. As the length of the steel bar indentation increases to a certain extent, the stress concentration at the end of the steel bar becomes more significant, causing local buckling of the specimen and ultimately leading to a sharp decrease in the bearing capacity of the specimen. To achieve reinforcement, facilitate construction, and ensure structural safety, the total indentation length Li of the steel bars should not be >2 cm in practical engineering projects. Considering the economy of reinforcement engineering, it is suggested that steel bars with smaller diameters, i.e., 6 mm, should be selected. Through a comparison between the FEA and the experimental process, it was confirmed that the failure model of the numerical model is basically consistent with the experimental phenomenon, and the bearing capacity deviation was <3.3%. This indicates that the FEM established in this study was reliable.