Advances in Structural Engineering最新文献

{"title":"Multi-experimental seismic analysis of low-rise thin reinforced concrete wall building with unconnected elastomeric isolators using real-time hybrid simulations","authors":"Bryan Castillo Torres, Eivar A Artunduaga Triviño, Johannio Marulanda Casas, Albert R Ortiz, Peter Thomson","doi":"10.1177/13694332241281525","DOIUrl":"https://doi.org/10.1177/13694332241281525","url":null,"abstract":"In response to the pressing need for housing and streamlining construction processes, the building industry has embraced innovative construction techniques. One such method, known as the Industrialized Housing Construction (IHC) system, departs from traditional framing systems by utilizing thin-reinforced concrete walls (TRCW). These TRCWs, characterized by high flowability and rapid strength gain, enable quick and efficient monolithic construction of walls and slabs. However, challenges have arisen regarding the structural behavior of these elements, potentially compromising their seismic performance. Given the significant seismic risk, there is a compelling need to develop resilient buildings by using this cost-efficient structural system. This study proposes the use of passive control systems such as base isolation to address this problem. While base isolation has proven effective in other countries, its feasibility in structures using TRCW and its performance during actual seismic events warrants further investigation. This paper presents an innovative approach using Multi-Axial Real-Time Hybrid Simulation (M-RTHS), which combines numerical and experimental components to gain deeper insights into the seismic response of low-rise TRCW buildings with base isolation using unconnected fiber-reinforced elastomeric isolators (U-FREIs). The methodology is detailed and includes the division of the structure into numerical and experimental segments and the use of transfer systems to replicate real seismic excitations, including those from El Centro (USA, 1940), Pizarro (Colombia, 2004), Chihuahua (Mexico, 2013), Loma Prieta (USA, 1989), and Kobe (Japan, 1995), with a maximum amplitude of 7.36 [Formula: see text] (0.75 g). The results highlight a remarkable reduction in upper structure floor drifts of over 57.47%, the characterization of the behavior and energy dissipation of each experimental specimen, and the optimal evaluation of M-RTHS. This research paves the way for improving the seismic resistance of buildings in regions prone to seismic activity, especially those using innovative construction methods such as TRCW.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based minute-scale digital prediction model for temperature induced deflection of a multi-tower double-layer steel truss bridge","authors":"Lingxin Meng, Bo Sun, Yingjie Dang, Lizhong Shen, Yizhou Zhuang","doi":"10.1177/13694332241281858","DOIUrl":"https://doi.org/10.1177/13694332241281858","url":null,"abstract":"Bridge deflection serves as a vital and intuitive index for the evaluation of bridge safety. Temperature load has the greatest influence on the bridge deformation and studies on the temperature-induced deformation prediction of long-span bridge are in limited numbers. A digital prediction model based on deep learning in minute scale is established to study the bridge deflection caused by temperature. The wavelet transform (WT) is adopted to filter the high-frequency signals of the original deflection caused by the related load factors. Three different networks, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and Transformer variant, are studied and compared in the prediction process. Two different learning strategies considering different input data are also considered to optimize the prediction performance. The proposed prediction model is applied to the temperature induced deflection prediction of a multi-tower double-layer steel truss bridge. The results show that strategy A, which employs temperature time series data as input, is less effective than strategy B. Incorporating both temperature and deflection data as inputs is essential for predicting temperature-induced deflections. Moreover, the Transformer-variant network generally exhibits superior prediction performance compared to the LSTM and Bi-LSTM. The self-attention mechanism of the Transformer allows it to focus on key historical temperature points, thereby enhancing prediction accuracy.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seismic response prediction method of train-bridge coupled system based on convolutional neural network-bidirectional long short-term memory-attention modeling","authors":"Xuebing Zhang, Xiaonan Xie, Han Zhao, Zhanjun Shao, Bo Wang, Qianqian Han, Yuxuan Pan, Ping Xiang","doi":"10.1177/13694332241281856","DOIUrl":"https://doi.org/10.1177/13694332241281856","url":null,"abstract":"Seismic response prediction is crucial for the safety analysis of train-bridge coupled systems. However, due to the complexity, suddenness, and high-risk nature of earthquakes, there are strong nonlinear relationships among different parts of bridges, making it challenging to express their spatial correlations using analytical models and traditional neural networks. To address this, this paper establishes a ballast track shaker scaling model and employs the grating monitoring measurement method to construct a spatial quasi-distributed monitoring system for the ballast track, thereby collecting seismic strain responses of the train-bridge coupled system under various seismic conditions. A hybrid neural network method is proposed for predicting the seismic responses of the train-bridge coupled system. This hybrid neural network integrates the features of a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory Neural Network (BiLSTM), and the attention mechanism, thereby termed the CNN-BiLSTM-attention hybrid neural network. The model was validated using strain responses from 54 seismic scenarios. The results indicate that the model has a Mean Absolute Error (MAE) of 0.2349 and a coefficient of determination (R<jats:sup>2</jats:sup>) of 0.9446. Comparing the prediction results with those from RNN and LSTM models, it was found that the CNN effectively extracts features under various seismic parameters, while the BiLSTM better captures the temporal information of the strain responses, ensuring effective prediction regardless of the magnitude of strain responses. Therefore, the CNN-BiLSTM-attention hybrid neural network model is recommended for predicting seismic response.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental investigation on shear behavior of double-row perforated GFRP rib connectors in FRP-concrete hybrid beams","authors":"Weichen Xue, Dawei Yan, Yongsheng Wang, Jiafei Jiang","doi":"10.1177/13694332241281549","DOIUrl":"https://doi.org/10.1177/13694332241281549","url":null,"abstract":"Perforated GFRP rib (PFR) connectors have been used in FRP-concrete hybrid beams due to durability and ease of construction. PFR connectors are parallel FRP plates with predrilled holes positioned in the flange of FRP beams. The optimal plate spacing needs to be determined because it affects the shear performance of PFR connectors. 18 push-out tests were conducted to investigate the effect of plate spacing ( S<jats:sub>l</jats:sub>), penetrating GFRP bar diameter ( d), and concrete strength ( f<jats:sub>c</jats:sub>) on the failure mode, capacity, and shear load-slip (P-S) curves of double-row PFR connectors. Results showed that PFR connectors suffered plate shear failure with the concrete dowel undamaged. Typical P-S curves consisted of micro-slipping and significant-slipping phases. The shear capacity and stiffness of PFR connectors were improved by 33.3% and 45.1%, respectively, by increasing the plate spacing from 1.2 h (where h denotes the plate height) to 3.2 h. The effect of plate spacing on shear capacity and stiffness could be neglected if the ratio ( S<jats:sub>l</jats:sub>/ h) was more than 3.2. Specimens with a larger diameter of penetrating bar and higher concrete strength demonstrated higher capacity and stiffness. An empirical equation based on the maximum stress failure criterion was proposed to estimate the capacity of PFR connectors, considering the plate spacing effect, and verified by available data. Additionally, a description of the P-S curve was developed and calibrated by the experimental results.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II","authors":"Hanwen Zhang, Jinlong Liu, Shiqi Wang, Keyu Chen, Lei Xu, Jiaxing Ma, Qinghe Wang","doi":"10.1177/13694332241281534","DOIUrl":"https://doi.org/10.1177/13694332241281534","url":null,"abstract":"Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators ( R<jats:sup>2</jats:sup>, R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R<jats:sup>2</jats:sup>, R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and t<jats:sub>w</jats:sub>, l<jats:sub>w</jats:sub> and f <jats:sup>′</jats:sup><jats:sub>c</jats:sub> are the key parameters affecting the shear capacity of the flanged shear wall.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shear performance and capacity of FRP reinforced concrete beams: Comprehensive review and design evaluation","authors":"Guanghao Mai, Zezhou Pan, Hao Zhen, Xuhua Deng, Chumao Zheng, Zhenye Qiu, Zhe Xiong, Lijuan Li","doi":"10.1177/13694332241276058","DOIUrl":"https://doi.org/10.1177/13694332241276058","url":null,"abstract":"To enhance the durability of concrete structures in harsh environments, replacing steel bars with FRP bars is judged a feasible method. The low elastic modulus and transverse strength of FRP bars result in the weak shear capacity of concrete beams reinforced with FRP bars. Reviewing and summarizing existing literature are important for addressing this challenge. The research progress on the shear behaviour of concrete beams reinforced with FRP bars was systematically described from two aspects in this paper. In the aspect of literature review, shear mechanisms and main influencing factors of concrete and FRP stirrups were summarized. In addition, achievements and shortcomings of existing literature were summarized, and potential research directions were pointed out. In the aspect of design method evaluation, a database containing 525 samples was established and calculation models of shear capacity of concrete beams reinforced with FRP bars in seven codes were evaluated. Evaluation parameters mainly included shear span ratio, effective height, and maximum strain of FRP stirrups. Based on the calculation model in Italian code CNR DT203-06, an optimized model was proposed to improve the accuracy in predicting concrete shear contribution. The review and evaluation in this paper had important reference significance for improving the design level of concrete structures reinforced with FRP bars and promoting the engineering application of FRP bars.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified fragility functions for offshore wind turbines considering soil-structure interaction subjected to wind, wave, and seismic loads","authors":"Leila Haj Najafi","doi":"10.1177/13694332241281537","DOIUrl":"https://doi.org/10.1177/13694332241281537","url":null,"abstract":"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.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study on fatigue damage mitigation in welded beam-to-column connections with passive vibration control","authors":"Fan Yang, Zhao Fang, Jianshao Zhang, Aiqun Li","doi":"10.1177/13694332241281531","DOIUrl":"https://doi.org/10.1177/13694332241281531","url":null,"abstract":"To investigate the effectiveness of a viscous fluid damper (VFD) in mitigating fatigue damage in welded beam-to-column connections, six specimens were manufactured with the full penetration groove weld process. These specimens were divided into three groups: S1, S2, and S3, each containing two specimens. One specimen in each group was equipped with VFD, while the other was not. All specimens underwent the same elastic cyclic loading stage, but a different plastic cyclic loading stage for each group. The study results indicate that the presence of VFD can significantly enhance the load-bearing capacity of the welded beam-to-column connection by 10%–15% when subjected to the same displacement amplitude at the beam end. The primary cause of failure in the welded beam-to-column connection is the fatigue damage of the weld toe on the beam flange, which requires more attention during the welding process. Additionally, the VFD can effectively reduce stress concentration at the weld seam area, leading to a maximum of 50% decrease in the maximum stress near the weld toe of the beam flange center, as observed in our study.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable machine learning approach for predicting the capacity and failure mode of reinforced concrete columns","authors":"May Haggag, Mohamed K. Ismail, Wael El-Dakhakhni","doi":"10.1177/13694332241281546","DOIUrl":"https://doi.org/10.1177/13694332241281546","url":null,"abstract":"During seismic events, reinforced concrete (RC) columns play a crucial role in maintaining buildings’ structural integrity. This motivated engineers and practitioners to search for key parameters that influence the load-carrying capacity and failure mechanisms of such columns. However, the complexity and nonlinearity of seismic effects along with the intricate nature of RC columns as a composite system challenge the capabilities of analytical and empirical approaches to accurately capture the response of RC columns. Subsequently, the present study utilizes Machine Learning (ML) techniques to identify the failure modes and predict the corresponding capacities of RC columns based on both their geometrical and material properties. Decision trees and different ensemble methods were employed to predict both the columns’ failure mode and ultimate capacity. A multivariate dataset consisting of 486 cyclically loaded rectangular and circular columns was used to develop and validate the models. In addition, different embedded variable selection techniques were employed to evaluate the significance of input parameters in predicting the performance of columns. Moreover, partial dependence plots and accumulated local effects were employed to uncover the interrelationships between the input features and the modelled outputs. The developed models yielded an average accuracy of 90% and 95% for predicting the failure mode and ultimate capacity of RC columns, respectively. Given such high accuracy, it can be inferred that, ML techniques have the potential to provide efficient and reliable prediction tools to support seismic design and assessment decisions - mitigating seismic risks and empowering resilience planning in the face of extreme events.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of seismic response amplification effects of diverse multi-support earthquake excitations on cable-stayed bridges","authors":"Hafiz Ahmed Waqas, Di Su, Tomonori Nagayama","doi":"10.1177/13694332241281523","DOIUrl":"https://doi.org/10.1177/13694332241281523","url":null,"abstract":"This study addresses the critical need to understand the seismic behavior of cable-stayed bridges under Multi-Support Excitation (MSE) in order to mitigate earthquake-induced damage to these structures. The primary focus is on the investigation of response amplification phenomena and their seismic implications for cable-stayed bridges. Through a detailed comparative analysis of MSE and Synchronous Excitation (SE) across various structural locations, the study evaluates the impact of site-specific recorded ground motions of different earthquake categories. A pragmatic framework is developed to simulate realistic MSE ground motions for diverse earthquake scenarios, emphasizing the necessity of considering MSE in bridge design. The findings reveal a significant amplification of the design requirements due to antisymmetric mode excitation and increased tower and pier motions. The study also identified the need for in-depth analysis of cable-stayed bridges to address the increased vulnerability of tower-adjacent areas and to devise targeted reinforcement strategies of vulnerable components. These insights are critical for advancing seismic design practices and improving the resilience of cable-stayed bridges, contributing to safer urban infrastructure.","PeriodicalId":50849,"journal":{"name":"Advances in Structural Engineering","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}