Engineering Structures最新文献

{"title":"Study on the response of a super high-rise guyed mast under synoptic winds based on aeroelastic wind tunnel test","authors":"Shikang Wu ,&nbsp;Muguang Liu ,&nbsp;Kang Liu ,&nbsp;Yuanpeng Pan ,&nbsp;Chunsheng Zhang ,&nbsp;Zhuangning Xie","doi":"10.1016/j.engstruct.2024.119201","DOIUrl":"10.1016/j.engstruct.2024.119201","url":null,"abstract":"<div><div>The present study examines the aeroelastic properties and wind-induced displacement response of a super high-rise guyed mast, through a series of aeroelastic model wind tunnel tests. The simulated guyed mast with a prototype height of 356 m is a representative guyed mast in Asia and has not been reported in previous literature. The impact of wind speed on both the alongwind and crosswind displacement response and the Gaussianity along the heights of the aeroelastic model is then investigated using wind tunnel measurements. Furthermore, the characteristics of mode participation and the effect of wind velocity on this phenomenon, as well as the contributions of background and resonant dynamic response, are examined in both the alongwind and crosswind directions. The results indicate that the guyed mast displacement distributions along the heights are different from those observed in the conventional self-standing lattice towers. The characteristics of multi-mode participation are notable. As wind speeds increase, the alongwind resonant response peaks shift to lower frequencies and become less distinct. In contrast, the crosswind response peaks show no significant variation. The contributions of background and resonant response along the heights, respectively, exhibit a decreasing and an increasing tendency, and wind speed exerts an influence on these phenomena.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119201"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual-based classification models for grading reclaimed structural timber for reuse: A theoretical, numerical and experimental investigation","authors":"Dag Pasquale Pasca ,&nbsp;Angelo Aloisio ,&nbsp;Yuri De Santis ,&nbsp;Hauke Burkart ,&nbsp;Audun Øvrum","doi":"10.1016/j.engstruct.2024.119218","DOIUrl":"10.1016/j.engstruct.2024.119218","url":null,"abstract":"<div><div>Among the key benefits of using structural timber is its potential for reuse after being dismantled from an existing building. Recycling and reuse are central concepts in the circular economy. However, the installation and dismantling of structural elements often leave traces from previous use, such as holes from connectors like dowels or screws, internal piping and cabling. Therefore, it is crucial to develop methods to rigorously quantify the reduced load-bearing capacity of recycled beams due to potential holes using efficient and expedited methods akin to visual grading approaches. This work proposes a visual-based method for classifying recycled timber based on the geometric characteristics of the artificial holes. A stochastic mechanics-based numerical model was developed to predict the bending strength reduction of beams with random hole patterns and thus generate an extensive dataset for calibrating data-driven binary classification models. Machine learning and conditional classification models are used to determine if the reduction in bending strength is greater or less than 20%, being the predefined threshold value for reduced strength grading. An experimental campaign on timber beams with specific hole patterns, determined after experimental design, led to the numerical model validation and the calibration of thresholds for the conditional classification model, which relies on a single feature: the sum of the diameters of the holes in two beam regions. The study shows that with an elementary conditional model, high-performance metrics of the binary classification model comparable to machine learning techniques can be achieved. In other words, with a balanced dataset, accuracies over 80% in classifying the level of capacity reduction, greater or lesser than 20%, can be achieved simply by comparing the sum of diameters to a predetermined threshold. This method currently fills a regulatory and methodological gap in safely reusing structural timber.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119218"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distribution of live load shears in FRP composite tub girder highway bridges","authors":"Jon Pinkham ,&nbsp;William G. Davids ,&nbsp;Andrew Schanck ,&nbsp;Keith Berube","doi":"10.1016/j.engstruct.2024.119188","DOIUrl":"10.1016/j.engstruct.2024.119188","url":null,"abstract":"<div><div>In the design of slab-on-girder highway bridges consisting of conventional materials like concrete and steel in the United States, the vehicular live load carried by a single girder is calculated using distribution factors (<span><math><mi>DF</mi></math></span>s) defined in the American Association of State Highway and Transportation Officials (AASHTO) design specifications. However, shear <span><math><mi>DF</mi></math></span>s for the recently developed fiber reinforced polymer composite tub (CT) girder do not exist within current design codes, and to-date in-service CT girder bridges have been designed using AASHTO shear <span><math><mi>DF</mi></math></span>s for concrete box girders. To assess shear live load distribution in CT girder bridges, diagnostic live load tests were performed on two in-service highway bridges under heavy truck loads. High-fidelity finite element (FE) models calibrated to the test results were simplified to reflect conventional design assumptions. The high-fidelity FE models indicated that AASHTO over-predicted live load shears in the most heavily loaded interior girder by as much as 35 %, but can under-predict exterior girder live load shear. Parametric studies using the simplified FE models indicated that while the most influential parameter on CT girder shear <span><math><mi>DF</mi></math></span>s is girder spacing, girder bottom flange width can also play a significant role. The simulations and diagnostic live load tests both indicate that the AASHTO shear <span><math><mi>DF</mi></math></span> expressions for concrete box, slab-on-girder bridges that are currently used in CT girder design typically over-predict shear <span><math><mi>DF</mi></math></span>s for interior CT girders. Simulations with the simplified model indicate over-predictions of <span><math><mi>DF</mi></math></span>s for interior CT girders of up to 30 % for longer spans and large girder spacing. However, in the CT girder that experienced the greatest shear strain during field load testing, measured strains in the most heavily loaded web were 22 % higher than the average girder web shear strain, a factor not currently accounted for by existing AASHTO <em>DF</em>s or in CT girder design.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119188"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized punching shear design in steel fiber-reinforced slabs: Machine learning vs. evolutionary prediction models","authors":"Asad S. Albostami ,&nbsp;Safaa A. Mohamad ,&nbsp;Saif Alzabeebee ,&nbsp;Rwayda Kh.S. Al-Hamd ,&nbsp;Baidaa Al-Bander","doi":"10.1016/j.engstruct.2024.119150","DOIUrl":"10.1016/j.engstruct.2024.119150","url":null,"abstract":"<div><div>This research paper focuses on utilizing Artificial Neural Networks (ANN), Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR), and Gene Expression Programming (GEP) to predict the punching shear strength of Steel Fibre-Reinforced Concrete (SFRC) slabs.</div><div>In order to formulate predictions, research and analysis were carried out making use of a dataset, this dataset included several parameters that impact on punching shear strength, including SFRC slabs longitudinally and transversely, using ANN, GEP, and MOGA-EPR methods. The developed models exhibited very good performance, as the soft computing techniques (GEP and MOGA-EPR) achieved <em>R</em>² values of 0.91 to 0.93, while the ANN technique was higher at 0.95. Furthermore, two case studies were incorporated to carry out cost analyses of the models in real-world applications. It was shown that the efficiency of the Machine Learning (ML) models in reducing the costs of materials is relatively high, as they were capable of better predictions than the standard methods employed by the codes.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119150"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on seismic performance of prestressed fabricated reinforced concrete frame structure assembled by steel sleeves","authors":"Jiaqi Liu,&nbsp;Dafu Cao,&nbsp;Kun Wang,&nbsp;Yanling Zhou,&nbsp;Hanyang Xue","doi":"10.1016/j.engstruct.2024.119222","DOIUrl":"10.1016/j.engstruct.2024.119222","url":null,"abstract":"<div><div>This paper introduces a novel type of prestressed fabricated reinforced concrete frame (PSFRC frame) structure, which utilizes steel sleeves for assembly. The PSFRC frame incorporates prestressed tendons, stiffened steel sleeves, and high-strength bolts, resulting in improved bearing capacity and assembly efficiency. To assess the seismic performance of the PSFRC frame joint, experimental tests were conducted on one reinforced concrete (RC) joint and two PSFRC frame joints under cyclic loading. Hysteresis analysis and elastic-plastic time-history analysis were also performed using a finite element model. The experimental results showed that the PSFRC edge joint reached a peak load of 89.30 kN, while the PSFRC middle joint exhibited a capacity of 169.95 kN, which was 58.87 % higher than that of the cast-in-place specimen XJ (107.87 kN). The hysteresis curves of the PSFRC joints demonstrated significant fullness, with the equivalent damping coefficient of the PSFRC joint being increased by 11.56 % compared to the RC joint. Additionally, a simulation method using ABAQUS software was proposed to investigate the seismic response of the integral structure of the PSFRC frame. Finite element models for both PSFRC and RC frames were established, and their seismic performance was analyzed under cyclic loading and the El Centro seismic wave. Various parameters including peak loading capacity, stiffness degradation, peak acceleration value, inter-storey drift ratio, and concrete damage value were considered. The finite element analysis results revealed that the load-carrying capacity of the PSFRC frame (435.33 kN) was approximately 30 % higher than that of the RC frame (386.48 kN). Furthermore, at the peak acceleration of 600 gal, the maximum inter-storey drift ratio of the first floor for the PSFRC frame was 1/58, which was below the limit value of 1/50. The plastic hinge position at the beam end of the PSFRC frame was further from the core area, aligning with seismic design objectives. This research provides valuable insights into the design of fabricated building structures and methods for analyzing seismic performance.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119222"},"PeriodicalIF":5.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the composite behavior developed through bond in the steel-concrete interface for future hot-rolled asymmetric steel I-beams","authors":"Chase Ottmers ,&nbsp;Robel Wondimu Alemayehu ,&nbsp;Matthew Yarnold","doi":"10.1016/j.engstruct.2024.119185","DOIUrl":"10.1016/j.engstruct.2024.119185","url":null,"abstract":"<div><div>Conventional steel-concrete composite floor systems consist of hot-rolled steel beams with metal decking on the top flange that supports a concrete deck slab. An alternative to achieve shallower floor depths is to utilize stay-in-place formwork, either precast concrete panels or steel deep decking, placed on the bottom flange with a cast-in-place concrete slab. For ease of construction, an asymmetric section is needed for vertical placement of the precast panels or deep decking. However, there are no hot-rolled asymmetric steel I-beams (termed A-shapes) readily available in the United States. The purpose of this research is to evaluate the composite flexural behavior of a shallow-depth floor system for future large-scale production of A-shapes. To achieve minimal floor depths and increase the efficiency of steel building construction, shear studs (generally used in conventional composite floor systems) are not utilized to transfer longitudinal interface forces. The top flange and web of the steel member are encased in concrete, which will allow partial composite flexural behavior due to the formation of concrete-steel bond. The research presented herein includes eight experimental beam tests to understand the flexural strength and stiffness developed through concrete-steel bond shear. The eight tests performed well and achieved 74 % to 83 % of the full composite flexural strength before the bond started to slip, although only minimally. Following the initial slip, the shallow-depth beams were unloaded and reloaded to evaluate the robustness and ductility of the composite cross-section. The beams proved to be highly ductile and robust as they reached 77 % to 91 % of the full composite strength upon reloading due to reengaging of the bond after the occurrence of initial slip. The composite flexural stiffness of the beams was well intact under the service loading, indicating that the transformed moment of inertia of the cross-section can be utilized for serviceability analysis. A bond shear strength of 0.69 MPa (100 psi) was established since it undercuts most of the experiments with a bond perimeter assumed to be above the elastic neutral axis of the composite section. The partial composite strength was further evaluated utilizing three numerical methods: (1) linear interpolation between steel yield and full composite, (2) partial plastic stress distribution, and (3) strain compatibility. All methods predicted reasonable results but the method of linear interpolation was the most conservative one in relation to the bending moment that causes the initiation of bond slip.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119185"},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 behavior of low-profile perfobond strip connectors for steel–thin UHPC composite deck structures","authors":"Xingyu Tan ,&nbsp;Zhi Fang ,&nbsp;Yibin Yin ,&nbsp;Renzhong Yuan ,&nbsp;Xinhua Liu ,&nbsp;Qi Liu","doi":"10.1016/j.engstruct.2024.119204","DOIUrl":"10.1016/j.engstruct.2024.119204","url":null,"abstract":"<div><div>This paper aims to investigate the shear performance of a low-profile perfobond strip (LPBL) connector embedded in a thin ultra-high-performance concrete (UHPC) layer of orthotropic steel–UHPC composite decks (OSUCDs). Push-out tests were performed on 20 specimens to determine the effects of overall dimensions of the strip, strip-end bearing, opening shapes, and perforating rebars. The corresponding shear transfer mechanism was examined through precise finite element (FE) analysis, and prediction methods for load–slip process and shear capacity were proposed through theoretical analysis. The results showed that LPBLs demonstrated satisfactory ductility with an ultimate slip exceeding 6 mm. The strip-end bearing and overall dimensions of the strip, particularly strip length and strip thickness, had significant effects on the shear capacity of LPBLs. As strip length and thickness increased, the weak link in shear capacity of LPBLs transitioned from the fracture of strip to the damage of UHPC dowel and strip-end compressive UHPC, and the contribution of perforating rebars progressively increased as the UHPC dowel's damage intensified. When opening areas were identical and ultimate capacity was controlled by UHPC dowels, circular hole LPBLs and those using wide holes had comparable shear performance, and the notch on the hole also had little effect. The configuration of notched wide holes was more conducive to the construction of LPBLs embedded in a thin UHPC layer. The proposed theoretical model based on LPBL's load transfer path predicted the load–slip curves well, and the accuracy of the proposed simplified equation for the shear capacity of LPBLs was also preliminarily validated by the test results.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119204"},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automation railway fastener tightness detection based on instance segmentation and monocular depth estimation","authors":"Weidong Wang ,&nbsp;Qiang Yin ,&nbsp;Chengbo Ai ,&nbsp;Jin Wang ,&nbsp;Qasim Zaheer ,&nbsp;Haoran Niu ,&nbsp;Benxin Cai ,&nbsp;Shi Qiu ,&nbsp;Jun Peng","doi":"10.1016/j.engstruct.2024.119229","DOIUrl":"10.1016/j.engstruct.2024.119229","url":null,"abstract":"<div><div>Railway fastener systems necessitate regular inspections to uphold the safety standards of high-speed trains. Previously, the capture of geometry characteristics and evaluation of fastener tightness relied on costly structured light cameras, falling short of meeting the growing demand for rapid and cost-effective detection. This study introduces a novel approach that amalgamates instance segmentation and monocular depth estimation, enabling fastener tightness inspection using a standard camera. The proposed method entails the following steps: Firstly, leveraging an enhanced ZoeDepth model, absolute depth is inferred from a single railway structure image to extract the vertical spatial features of the fastener system. Secondly, the YOLOv8 network is deployed to delineate the fastener elastic clip and bolt in the railway structure images, producing masks that facilitate depth distribution computation. Thirdly, by fusing the absolute depth maps and masks, apparent depth distribution features are computed utilizing the proposed metrics. These features undergo analysis and comparison with an online updated threshold library, facilitating the identification of loose fasteners. In this study, the collected Railway Structure Intensity-Depth dataset was used for model training, while on-site experiments were conducted to evaluate the accuracy of the proposed method. The experimental findings demonstrate that this method adeptly identifies loose fasteners, achieving a detection rate of 86.2 %.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119229"},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Layered stiffness detection of ballastless track based on loading force and multiple displacements","authors":"Shuaijie Miao ,&nbsp;Liang Gao ,&nbsp;Tao Xin ,&nbsp;Hui Yin ,&nbsp;Yonggui Huang ,&nbsp;Hong Xiao ,&nbsp;Xiaopei Cai","doi":"10.1016/j.engstruct.2024.119230","DOIUrl":"10.1016/j.engstruct.2024.119230","url":null,"abstract":"<div><div>Grasping the track stiffness status is significant to railway maintenance. However, the research on the data collection and detection method of ballastless track layered stiffness is lacking and challenging. This article proposes a data collection strategy for layered stiffness detection based on loading force and multiple displacements. The dataset, which consists of loading force and multiple displacements collected along the railway line, effectively reflects track layered stiffness, including the overall track stiffness and the slab upper and bottom stiffness. The stiffness detection data is input into the BP model optimized by particle swarm optimization (PSO-BP) to mine the correlation between different sublayer defects, track layered stiffness' fluctuation, and then predict the layered stiffness sequences and locate local anomalies. On this basis, an image dataset of 25 abnormal layered stiffness cases is constructed, caused by different degrees of abnormal fastener stiffness, mortar void, subgrade settlement and their overlap. The Resnnet18 model, pre-trained by transfer learning, is used to identify layered stiffness anomaly cases in image datasets, and the accuracy is 94.63 %.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119230"},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Damage detection in lightweight bridges with traveling masses using machine learning","authors":"Georgios Dadoulis ,&nbsp;George D. Manolis ,&nbsp;Konstantinos Katakalos ,&nbsp;Kosmas Dragos ,&nbsp;Kay Smarsly","doi":"10.1016/j.engstruct.2024.119216","DOIUrl":"10.1016/j.engstruct.2024.119216","url":null,"abstract":"<div><div>Damage detection via vibration testing typically relies on damage-sensitive features, which serve as “damage indicators”, and decisions upon the existence of damage are based on comparing the damage indicators retrieved from two distinct structural states. However, the relatively low sensitivity of damage indicators to the onset of structural damage remains an open question, despite the considerable research efforts in vibration testing over the years. Low-sensitivity problems may be particularly exacerbated by the complex dynamic behavior of lightweight structures, such as lightweight bridges subjected to vehicular traffic. In particular, due to material (and, by extension, mass) reduction in lightweight bridges, vehicles essentially act as “traveling masses”, which are comparable to the structural mass and result in a coupled complex dynamic motion problem that may obscure typical damage indicators used in vibration testing. This paper presents a damage detection approach for lightweight bridges with traveling masses, leveraging the powerful feature-extraction capabilities of machine learning (ML). In particular, a convolutional neural network (CNN) is trained to classify acceleration response data, collected from vibration testing, into damage scenarios. The training data for the CNN are created via simulations of damage scenarios, using calibrated analytical models. The damage detection approach is validated in laboratory tests on a continuous beam, showcasing the capability of the CNN to classify damage scenarios of the beam. The outcome of this paper aims to serve as a starting point towards employing ML for damage detection in the context of vibration testing as well as structural health monitoring.</div></div>","PeriodicalId":11763,"journal":{"name":"Engineering Structures","volume":"322 ","pages":"Article 119216"},"PeriodicalIF":5.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}