查看更多>>摘要：High-damping rubber bearings (HDRBs) are among the most widely used seismic isolators in bridge and building engineering. However, they exhibit significant nonlinear behavior, particularly under large-strain deformation. Key nonlinearities include large-strain hardening, degradation, temporary hardening, and initial hardening. Incorporating all these characteristics into a single macroscopic model is challenging. This study reviews the most relevant and accurate models for HDRBs and highlights their significant features. To address the limitations in existing models, an improved Bouc-Wen (IBW) model is proposed and implemented in OpenSees. Reversed loading cycles were conducted on HDRB specimens, and the widely adopted deformation history integral (DHI) model is also introduced for comparative analysis. The IBW model is validated by comparing it with DHI model and experimental results, with comparisons focused on the shape of hysteretic loops, equivalent stiffness, and damping ratios. Results demonstrate the applicability and accuracy of the proposed numerical element in modeling HDRBs under displacement-controlled reversed loading. The IBW model proves more concise and effectively represents the primary nonlinearities of HDRBs. Overall, it outperforms the DHI model in scenarios involving large strains and initial loading cycles, while the DHI model shows slightly higher accuracy in low-to- moderate strain cycles. Future research should investigate strength degradation under prolonged loading and the dynamic response of both bearings and isolated superstructures under seismic loading.
查看更多>>摘要：This study introduces a practical and easy-to- implement method for detecting cracks in concrete structures that meets engineering application requirements. The approach integrates a distance sensor with an image perspective distortion correction algorithm and employs deep learning techniques to automatically correct image distortions, extract crack information, and quantify the crack condition. This method addresses the inadequacies of previous studies in considering perspective distortion, enabling automated and efficient image capture and accurate crack identification. First, we fine-tuned a lightweight object detection model based on the pre-trained YOLOv8x model within the YOLOv8 framework, using a custom dataset to enhance its performance. Next, we trained a semantic segmentation deep learning model using several public datasets containing 9584 crack images and their corresponding pixel-level annotations for precise crack detection. Additionally, a distance sensor combined with a calibration and image processing algorithm was used to remove perspective distortion and convert the size of structural defects from pixels to millimeters. This process includes capturing component edges, calculating the aspect ratio, and performing perspective correction, ensuring high accuracy in images of cracked structures taken from various angles. Field tests showed that this method improves crack detection accuracy and measurement precision under correct conditions. It simplifies the detection process and offers a reliable automated solution, enhancing the efficiency and accuracy of crack monitoring in concrete structures.
Ali EjtemaeeHedayat VeladiBahman Farahmand AzarHosein Ghaffarzadeh...
1-24页
查看更多>>摘要：This paper addresses the pursuit of robust hybrid control for seismically excited structures, employing structural models featuring devices such as Viscous- Fluid-Dampers integrated with Chevron bracing, Base-Isolators, and Active- Controllers, utilized in passive, active, and hybrid control configurations. Motion equations were formulated in state space, facilitating simulations to assess nominal performance. Primitive parametric uncertainty was defined to ascertain stability and performance margins of passive control systems. Robust parameters of the selected control system were determined, followed by the derivation of maximum parametric uncertainty ranges and the design of an Hꝏ-based active controller for an uncertain Closed-Loop system. Time histories of floor displacements, damper force, and active controller power were analyzed. Our examination revealed that parametric uncertainties vary based on the type, physical characteristics, and placement of passive control devices. In robust controlled systems, the utilization of an active controller significantly reduced the reliance on passive control equipment, while non-robust systems exhibited divergent seismic behaviors. Overall, the results demonstrate the effectiveness of hybrid control strategies in mitigating seismic responses, with the incorporation of base isolators extending the range of parametric uncertainties.
查看更多>>摘要：This research presents a novel hybrid technique to forecast the mechanical properties of glass wool fiber reinforced concrete (GWFRC) using progressive graph convolutional networks (PGCN) and sand cat swarm optimization (SCSO), termed as PGCN-SCSO. The primary goal is to optimize the composition of GWFRC by accurately forecasting its compressive, flexural, and tensile strengths. The PGCN model learns complex relationships between input features, while the SCSO algorithm optimizes the hyperparameters of the PGCN for enhanced prediction accuracy. Experimental data from M20 and M30 grade concrete mixtures, incorporating glass wool fibers (0.5%-3%) and 30% ground granulated blast furnace slag (GGBS) replacement, were used to validate the proposed approach. The model's performance was assessed using the mean absolute error (MAE), coefficient of determination (R2), and root mean square error (RMSE), showing better outcomes than established methods such as artificial neural network (ANN), genetic algorithm-extreme gradient boosting (GA-XGBoost), and light gradient boosting machine (LightGBM). The PGCN-SCSO approach achieved R2 values of 0.94, 0.99, and 0.98 for flexural, compressive and tensile strength predictions, respectively, indicating its effectiveness in accurately predicting GWFRC properties and optimizing concrete formulations.
Farhad ShahidiArmin AziminejadMehran S. RazzaghiFariborz Nateghi-A...
1-27页
查看更多>>摘要：A new type of yielding damper, "dual octagonal section" (DOS), for use in the self-centering moment and braced frames, is developed and numerically evaluated. In the new damper, the interconnection of two octagonal sections to each other is done by groove welds. Also, high-strength bolts connect them to the support plates to facilitate the replacement and removal of workshop welding. After verification with experimental specimens, numerical evaluations have been done through nonlinear finite element modeling with full details. Elastic stiffness, yielding force and displacement, internal forces of bolts, and other theoretical formulas of design are presented. In this evaluation, the behaviors of DOS dampers under monotonic and cyclic loading by nonlinear finite elements have been fully evaluated. The results of the evaluations show that the stable hysteresis curves and high performance in energy dissipation and ductility, along with the its light weight, ease of fabrication and installation, and removal of the workshop welding, make this new type of damper suitable for use in braced frames and connections of self-centering steel moment frames. Finally, it is recommended that to achieve optimal performance and executive provisions, some parameters and geometrical specifications be limited.
查看更多>>摘要：Although box columns offer significant advantages, they also have limitations, particularly regarding the design of connection plates and their shear strength at connections. The introduction of ConXL, recognized as a prequalified solution under AISC regulations, addresses these issues. This well-known connection offers several benefits, including improved industrialization and quality control, elimination of continuity plates, faster construction times, and streamlined inspections. This paper presents an innovative approach that utilizes T-stubs to enhance the performance of ConXL with unfilled box columns. The study evaluates the behavior of both conventional and enhanced ConXL connections under ambient conditions as well as elevated temperatures to assess their seismic performance and resistance after fire exposure. The findings reveal that all variants of ConXL exhibited stable hysteresis curves. Even when exposed to temperatures up to Tu=600℃, these connections maintained a rotational capacity exceeding 0.04 rad without developing plastic hinges at the joints. It is feasible to remove concrete if it leads to a necessary increase in column thickness; an appropriate equation for this adjustment was proposed. Additionally, incorporating T-stubs significantly improved the behavior of ConXL connections at higher temperatures. Comparative analyses showed that the use of T-stubs increased the ultimate strength of the system by factors of 1.08, 1.11, 1.10, and 1.87 for Tu=20℃, Tu=200℃, Tu=400℃ , and Tu=600℃, respectively. Furthermore, an equation was proposed for predicting system behavior based on these findings.
查看更多>>摘要：Real-time hybrid simulation (RTHS) offers a cost-effective method for evaluating the dynamic response of structural systems. However, multi-axis RTHS (maRTHS) faces challenges, including time delays, actuator coupling, and system uncertainties, which can compromise its accuracy and even stability. To address these issues, this paper proposes a robust two-stage actuator control approach, which leverages a H∞ control strategy combined with an inverse compensator. In the first stage, a H∞ controller is designed based on the mixed-sensitivity functions to address modeling uncertainties, parameter uncertainties, and external disturbances. This ensures the RTHS system's stable tracking performance, robust characteristics, and decoupling among different actuators. The second stage uses an inverse compensator individually for each actuator, further reducing system time delay and enhancing tracking accuracy for the nearly decoupled system. This methodology is illustrated through a maRTHS benchmark problem. Virtual RTHS results show that the proposed robust two-stage control method not only achieves excellent tracking performance but also has robustness under diverse operating conditions.
查看更多>>摘要：The development of methodologies for vulnerability assessment of high-rise structures is still in the early stages due to the computational toll of the whole process. This paper investigates the efficacy of vulnerability information of high-rise tubular structures, developed by means of two different analytical modeling approaches. The first approach is based on the modal decomposition to simplify the convoluted nonlinear 3D analytical model, and correspondingly, four nonlinear single-degree- of- freedom (SDOF) systems have been established to conduct the nonlinear dynamic analysis, considering the modal mass participation ratio of more than 90%. In the second modeling approach, unsupervised machine learning (ML), that is, genetic algorithms (GAs), has been implemented for extracting the structural modeling parameters from a fully nonlinear CSI Perform 3D model to establish a GA-based simplified model. The incremental dynamic analysis (IDA) results from both modeling approaches were processed, and eventually, the fragility curves were established to depict the structural vulnerability of high-rise tubular structures. A rational comparison of the fragility information and the total time consumed for both of the processes has been made to infer their practical applicability. Results reveal that both methods yielded adequate results with some disparities, attributed to the differences in the modeling processes. Both structural analytical modeling methodologies can be effectively employed for predicting the seismic vulnerability information of tubular structures after comprehending their fundamental differences presented in this paper.
查看更多>>摘要：In recent years, the investigation into the damping performance of hybrid damping devices has garnered significant attention, particularly a hybrid damper composed of a tuned mass damper (TMD), and an inerter has been favored for their superior damping capabilities. In the work, novel solutions for spectral moments of wind-induced responses of high-rise structures with tuned parallel inerter mass system (TPIMS) were proposed. Additionally, an optimization method for TPIMS's parameters was presented, considering various structural responses to evaluate dynamic reliability and comfort. First, based on dynamic finite element technique, we established a comprehensive dynamic equation for high-rise structures integrated with TPIMS, addressing the challenge of obtaining mass and stiffness matrices in traditional dynamic equations. Second, using quadratic decomposition method for power spectrum density function and Gauss-Jacobi integration method, concise unified closed-form solutions of zero-, first-, and second-order spectral moments (ZFS-OSMs) of outputs were deduced. Third, leveraging the proposed ZFS-OSMs solutions, we applied Vanmarcke's method to optimize TPIMS parameters for enhanced dynamic reliability and comfort. Finally, three case studies were presented: one validating the proposed ZFS-OSMs solutions, another examining the influence of real vibration modes on calculation accuracy, and a third demonstrating the efficacy of the TPIMS parameter optimization method. The results indicate that the selection of real mode shapes must be tailored to the analyzed output, rather than assuming a small number of modes suffices. The proposed optimization approach comprehensively considers multiple responses from high-rise structures, offering valuable insights applicable to the analysis of other passive dampers.
查看更多>>摘要：Concrete is a very adaptable building material made of cement paste and aggregates. Concrete is vital in the construction sector because of its strength, durability, affordability, and versatility. Tensile strength and material durability are two important considerations for engineers and builders when constructing buildings. Fiber-reinforced concrete (FRC) is a well-known type of concrete that uses synthetic and natural fibers to strengthen its mechanical properties. The conventional approaches for concrete testing are also covered in this study. These methods frequently entail sophisticated laboratory apparatus and call for specific knowledge. With the development of technology, new concrete strength prediction techniques have emerged to provide more accurate and efficient ways. This paper reviews the strength and durability testing for various FRC to assess its mechanical properties like compressive, flexural, and tensile strength, as well as its resistance to environmental factors like freeze-thaw cycles and chemical attack. It also explores the application of artificial intelligence (AI) to predict the performance and behavior of FRC in various applications, offering advantages over traditional methods due to their ability to handle complex data and relationships. In AI, machine learning (ML) and deep learning (DL) models, which have major advantages for the construction industry, are also analyzed.
NETL
NSTL
Wiley