Journal of Infrastructure Intelligence and Resilience最新文献_第9页

inside back cover: using Editorial Board page 内封底:使用编委会页面

Journal of Infrastructure Intelligence and Resilience Pub Date : 2022-09-01 DOI: 10.1016/j.iintel.2022.100014

引用次数: 0

Journal of Infrastructure Intelligence and Resilience Pub Date : 2022-09-01 DOI: 10.1016/j.iintel.2022.100007

Yaozhi Luo, Michael Havbro Faber, Yi-Qing Ni, Andrew W. Smyth

引用次数: 0

Defect-mode-induced energy localization/harvesting of a locally resonant phononic crystal plate: Analysis of line defects 局部共振声子晶体板的缺陷模式诱导能量局域化/收获:线缺陷分析

Journal of Infrastructure Intelligence and Resilience Pub Date : 2022-09-01 DOI: 10.1016/j.iintel.2022.100001

Dong-Xing Cao , Sha-Sha Li , Chang-Hai Zhan , Yi-Ming Lu , Jia-Jia Mao , Siu-Kai Lai

{"title":"Defect-mode-induced energy localization/harvesting of a locally resonant phononic crystal plate: Analysis of line defects","authors":"Dong-Xing Cao , Sha-Sha Li , Chang-Hai Zhan , Yi-Ming Lu , Jia-Jia Mao , Siu-Kai Lai","doi":"10.1016/j.iintel.2022.100001","DOIUrl":"10.1016/j.iintel.2022.100001","url":null,"abstract":"<div><p>Phononic crystals that are artificially engineered structures have recently been introduced for vibration energy harvesting and sensing applications due to their unique features of band gaps and wave propagation control. Conventional energy harvesters made of phononic crystals are mainly designed for acoustic energy harvesting at a high-frequency vibration source (i.e., kHz levels). In this work, a defect-mode-induced energy harvester is designed for low-frequency excitations in the range of 0–300 Hz. The entire system that is a locally resonant phononic crystal (LRPC) plate with line defect patterns is consisted of elastic-wrapped core scatterers periodically embedded in epoxy resin. A two-dimensional (2D) three-component unit cell structure is arranged on the plate and the band gap property is analyzed to optimize the geometric parameters. Defects are then introduced to the LRPC plate with a 7 × 7 point array for analysis. In addition, numerical and experimental studies are conducted to investigate the performance of energy harvesting when attaching a piezoelectric patch on the defect points. The results demonstrate that the proposed LRPC vibration energy harvester having a line defect mode (with continuous or alternate points) shows good performance in energy harvesting, in which a peak power output of 42.72 mV can be achieved under 10 m/s<sup>2</sup> and 252 Hz. The performance is almost 6 times more than that of the single-point defect model under the same excitation conditions. The present LRPC-type energy harvester with a line defect mode is more suitable for energy harvesting for low-frequency and broadband conditions.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"1 1","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991522000019/pdfft?md5=b938ae73514bc4239591024bf8ace575&pid=1-s2.0-S2772991522000019-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81830318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A graphics-based digital twin framework for computer vision-based post-earthquake structural inspection and evaluation using unmanned aerial vehicles 基于图形的基于计算机视觉的无人机地震后结构检测与评估数字孪生框架

Journal of Infrastructure Intelligence and Resilience Pub Date : 2022-09-01 DOI: 10.1016/j.iintel.2022.100003

Shuo Wang , Casey Rodgers , Guanghao Zhai , Thomas Ngare Matiki , Brian Welsh , Amirali Najafi , Jingjing Wang , Yasutaka Narazaki , Vedhus Hoskere , Billie F. Spencer Jr.

{"title":"A graphics-based digital twin framework for computer vision-based post-earthquake structural inspection and evaluation using unmanned aerial vehicles","authors":"Shuo Wang , Casey Rodgers , Guanghao Zhai , Thomas Ngare Matiki , Brian Welsh , Amirali Najafi , Jingjing Wang , Yasutaka Narazaki , Vedhus Hoskere , Billie F. Spencer Jr.","doi":"10.1016/j.iintel.2022.100003","DOIUrl":"10.1016/j.iintel.2022.100003","url":null,"abstract":"<div><p>Rapid structural inspections and evaluations are critical after earthquakes. Computer vision-based methods have attracted the interest of researchers for their potential to be rapid, safe, and objective. To provide an end-to-end solution for computer vision-based post-earthquake inspection and evaluation of a specific as-built structure, the concepts of <em>physics-based graphics model</em> (PBGM) and <em>digital twin</em> (DT) are combined to develop a <em>graphics-based digital twin</em> (GBDT) framework. The GBDT framework comprises a <em>finite element</em> (FE) model and a <em>computer graphics</em> (CG) model whose state is informed by the FE analysis, representing the state of the structure before and after an earthquake. The CG model is first created making use of the FE model and the photographic survey of the structure, yielding the virtual counterpart of the as-built structure quickly and accurately. Then damage modelling approaches are proposed to predict the location and extent of structural and nonstructural damage under seismic loading, from which photographic representation of the predicted damage is realized in the CG model. The effectiveness of the GBDT framework is demonstrated using a five-story reinforced concrete benchmark building through the design and assessment of various UAV (<em>Unmanned Aerial Vehicle</em>) inspection trajectories for post-earthquake scenarios. The results demonstrate that the proposed GBDT framework has significant potential to enable rapid structural inspection and evaluation, ultimately leading to more efficient allocation of scarce resources in a post-earthquake setting.</p></div>","PeriodicalId":100791,"journal":{"name":"Journal of Infrastructure Intelligence and Resilience","volume":"1 1","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772991522000032/pdfft?md5=26d66d2828382c88e6934fe393e0bcfc&pid=1-s2.0-S2772991522000032-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85018638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9