SDHM Structural Durability and Health Monitoring最新文献

{"title":"Artificial Neural Network (ANN) Approach for Predicting Concrete Compressive Strength by SonReb","authors":"M. Bonagura, L. Nobile","doi":"10.32604/SDHM.2021.015644","DOIUrl":"https://doi.org/10.32604/SDHM.2021.015644","url":null,"abstract":"The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures. According to various international codes, core samples are drilled and tested to obtain the concrete compressive strengths. Non-destructive testing is an important alternative when destructive testing is not feasible without damaging the structure. The commonly used non-destructive testing (NDT) methods to estimate the in-situ values include the Rebound hammer test and the Ultrasonic Pulse Velocity test. The poor reliability of these tests due to different aspects could be partially contrasted by using both methods together, as proposed.in the SonReb method. There are three techniques that are commonly used to predict the compressive strength of concrete based on the SonReb measurements: computational modeling, artificial intelligence, and parametric multi-variable regression models. In a previous study the accuracy of the correlation formulas deduced from the last technique has been investigated in comparison with the effective compressive strengths based on destructive test results on core drilled in adjacent locations. The aim of this study is to verify the accuracy of Artificial Neural Approach comparing the estimated compressive strengths based on NDT measured parameters with the same effective compressive strengths. The comparisons show the best performance of ANN approach.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69902769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems","authors":"S. Kalyani, K. Venkata Rao, A. Mary Sowjanya","doi":"10.32604/sdhm.2021.016975","DOIUrl":"https://doi.org/10.32604/sdhm.2021.016975","url":null,"abstract":"Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration. Sensor data of all possible states of a system are used for building machine learning models. These models are further used to predict the possible downtime for proactive action on the system condition. Aircraft engine data from run to failure is used in the current study. The run to failure data includes states like new installation, stable operation, first reported issue, erroneous operation, and final failure. In the present work, the non-linear multivariate sensor data is used to understand the health status and anomalous behavior. The methodology is based on different sampling sizes to obtain optimum results with great accuracy. The time series of each sensor is converted to a 2D image with a specific time window. Converted Images would represent the health of a system in higher-dimensional space. The created images were fed to Convolutional Neural Network, which includes both time variation and space variation of each sensed parameter. Using these created images, a model for estimating the remaining life of the aircraft is developed. Further, the proposed net is also used for predicting the number of engines that would fail in the given time window. The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components. Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69903076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Twin-Driven Intelligent Construction: Features and Trends","authors":"Hao Zhang, Yongqi Zhou, Huaxin Zhu, D. Sumarac, Maosen Cao","doi":"10.32604/sdhm.2021.018247","DOIUrl":"https://doi.org/10.32604/sdhm.2021.018247","url":null,"abstract":"","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69903238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental and Numerical Assessment on Seismic Performance of Earth Adobe Walls","authors":"Zele Li, Mohammad N. Noori, Wael A. Altabey","doi":"10.32604/SDHM.2021.011193","DOIUrl":"https://doi.org/10.32604/SDHM.2021.011193","url":null,"abstract":"Earth buildings are common types of structures in most rural areas in all developing countries. Catastrophic failure and destruction of these structures under seismic loads always result in loss of human lives and economic losses. Wall is an important load-bearing component of raw soil buildings. In this paper, a novel approach is proposed to improve the strength and ductility of adobe walls. Three types of analyses, material properties, mechanical properties, and dynamic properties, are carried out for the seismic performance assessment of the adobe walls. These performed studies include that, material properties of the earth cylinder block, mechanical properties of adobe walls under quasi-static loads, and dynamic performance of adobe walls excited by seismic waves. On investigation of material properties, eighteen cylindrical specimens with a diameter of 100 mm and a height of 110 mm were divided into three groups for compressive, tensile, and split pull strength tests, respectively. The results of the three groups of tests showed that the yield strength ratios of compressive, tensile, and shear strength were about 1:0.3:0.2. In order to study the performance of structural components, three 1/3 scale model raw soil walls with a dimension of 1,200 mm in width, 1,000 mm in height, and 310 mm in thickness were tested under cyclic loading. The average wall capacity of the wall obtained by the test was about 13.5 kN and the average displacement angle was about 1/135. The numerical simulation experiment is used to explore the mechanism of structural failure. A three-dimensional finite element model is established by choosing the material parameters based on the above test outcomes. The accuracy of the numerical simulation experiment is verified by simulation and comparison of the above quasi-static test results. Further, the collapse process of raw soil wall under a seismic wave is simulated for exploring the response and damage mechanism of structure. Based on those systematically analyzed, some useful suggested guidelines are provided for improving the seismic performance of raw soil buildings.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69901970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study on Compressive Strength of Recycled Aggregate Concrete under High Temperature","authors":"M. Akhtar, Abdulsamee M. Halahla, Amin H. Almasri","doi":"10.32604/sdhm.2021.015988","DOIUrl":"https://doi.org/10.32604/sdhm.2021.015988","url":null,"abstract":"This research aims to study the effect of elevated temperature on the compressive strength evolution of concrete made with recycled aggregate. Demolished building concrete samples were collected from four different sites in Saudi Arabia, namely from Tabuk, Madina, Yanbu, and Riyadh. These concretes were crushed and recycled into aggregates to be used to make new concrete samples. These samples were tested for axial compressive strength at ages 3, 7, 14, and 28 days at ambient temperature. Samples of the same concrete mixes were subjected to the elevated temperature of 300°C and tested for compressive strength again. The experimental result reveals that the recycled aggregate concrete samples have good quality at ambient and elevated temperatures and are considered fairly close to the concrete made with natural aggregate. However, recycled aggregate concrete at high temperatures showed higher strength degradation than natural aggregate concrete, but with differences that do not exceed 5% to 10%. The concrete samples made from recycled coarse aggregates also reached the design strength. It can be considered acceptable, considering the high variation in the concrete’s thermal response found in the literature.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69902828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fatigue Performance of Orthotropic Steel Decks in Super-Wide Steel Box Girder Considering Transverse Distribution of Vehicle Load","authors":"Xudong Wang, C. Miao, Mao Yang, You-liang Ding","doi":"10.32604/sdhm.2021.017526","DOIUrl":"https://doi.org/10.32604/sdhm.2021.017526","url":null,"abstract":"This study presents an investigation on the fatigue analysis of four types of details on orthotropic steel decks (OSDs) for a cable-stayed super-wide steel box girder bridge based on finite-element analysis (FEA) with vehicle transverse distribution model (VTDM). A high-fidelity 3D FE model verified by the static load test is established to satisfy the fatigue analysis accuracy. The stress behavior of super-wide steel box girders under the vehicle load at different lane locations is investigated. Then, considering the effect of VTDM, the fatigue life analysis of four typical details is performed using the Miner cumulative damage rule. The results show that the vehicle transverse location has a great influence on the stress behavior of details with sharp influence surface, and the stress ranges in the outermost lane are larger than those in other lanes, indicating that the details of OSD in the outermost lane are prone to fatigue. The fatigue life analysis indicates that the diaphragm cutout is more prone to fatigue than other details, which should be carefully treated in bridge maintenance.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69903203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Reconstruction Technology of Flexible Structure Shape Based on FBG Sensor Array and Deep Learning Algorithm","authors":"K. Huang, Jie Yan, Lei Zhang, Faye Zhang, Mingshun Jiang, Q. Sui","doi":"10.32604/sdhm.2022.018202","DOIUrl":"https://doi.org/10.32604/sdhm.2022.018202","url":null,"abstract":"","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69903702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite Element Model Updating for Structural Health Monitoring","authors":"A. Haidarpour, K. Tee","doi":"10.32604/sdhm.2020.08792","DOIUrl":"https://doi.org/10.32604/sdhm.2020.08792","url":null,"abstract":"This paper provides a model updating approach to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration responses. Research in vibration-based damage identification has been rapidly expanding over the last few decades. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause changes in the modal properties which could be obtained by structural health monitoring (SHM). Updating is a process fraught with numerical difficulties. These arise from inaccuracy in the model and imprecision and lack of information in the measurements, mainly taken place in joints and critical points. The motivation for the development of this technology is presented, methods are categorized according to various criteria such as the level of damage detection provided from vibration testing, natural frequency and mode shape readings are then obtained by using modal analysis techniques, which are used for updating structural parameters of the associated finite element model. The experimental studies for the laboratory tested bridge model show that the proposed model updating using ME’scope technique can provide reasonable model updating results.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69901545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-Term Bending Behaviour of Prestressed Glulam Bamboo-Wood Beam\u0000Based on Creep Effect","authors":"Guo Nan, Huajing Xiong, Mingtao Wu, Hongliang Zuo, Feng Jiang, Xiaofeng Hou, D. Xin","doi":"10.32604/sdhm.2020.09104","DOIUrl":"https://doi.org/10.32604/sdhm.2020.09104","url":null,"abstract":": Creep is an important characteristic of bamboo and wood materials under long-term loading. This paper aims to study the long-term bending behaviour of prestressed glulam bamboo-wood beam (GBWB). For this, 14 pre-stressed GBWBs were selected and subjected to a long-term loading test for 60 days. Then, a comparative analysis was performed for the effects of pretension values, the number of pre-stressed wires, and long-term load on the stress variation of the steel wire and the long-term de fl ection of the beam midspan. The test results showed that with the number of prestressed wires increasing, the total stress of the steel wire in the beam midspan and the ratio of the long-term de fl ection to the total de fl ection decreases decreased, but when the number of steel wires exceeded 4, the total stress and long-term de fl ection was less in fl uenced; with the pre-tension value increasing, the ratio of the total stress of the steel wire in the beam midspan and the ratio of the long-term de fl ection to the total de fl ection also decreased, but when the prestress force was greater than 3.975 kN, the total stress and long-term de fl ection were less affected; with the other parameters unchanged, when the value of the long-term load increased, the total stress of the steel wire decreased, and the long-term de fl ection of the beam midspan increased, which shall be more signi fi cant with the long-term load greater than 30% of the standard ultimate bearing capacity. After the test, the experimental data were fi tted, and the creep coef fi cient was given. Finally, the long-term stiffness calculation formula of the pre-stressed GBWB based on creep effect was proposed. The research fi ndings have certain theoretical signi fi cance and engineering value.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69901632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Concrete Beam Crack Recognition Algorithm Based on Block\u0000Threshold Value Image Processing","authors":"Wenting Qiao, Xiaoguang Wu, Wen Sun, Qiande Wu","doi":"10.32604/sdhm.2020.011479","DOIUrl":"https://doi.org/10.32604/sdhm.2020.011479","url":null,"abstract":": To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven illumination and complex surface color of concrete structure, this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system. The steps in this research are as follows: 1. The crack digital images of concrete specimens with typical features were collected by using the Actis system of KURABO Co., Ltd., of Japan in the concrete beam bending test. 2. The images are segmented into blocks to distinguish backgrounds of different grayscale. 3. The maximum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented. 4. Segmentation is made to the image with 2D maximum entropy threshold segmentation method to obtain the binary image, and the target image can be obtained by screening the connected domain features of the binary image. Results have shown that compared with other algorithms, the proposed method can effectively decrease the image over-segmentation and under segmentation rates, highlight the characteristics of the target cracks, solve the problems of excessive difference between the identi fi ed length and actual length of cracks caused by background gray level change and uneven illumination, and effectively improve the recognition accuracy of bridge concrete cracks.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69901294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}