International Conference on Smart Infrastructure and Construction 2019 (ICSIC)最新文献_第2页

An Annotation Tool for Benchmarking Methods for Automated Construction Worker Pose Estimation and Activity Analysis 一种用于自动化建筑工人姿态估计和活动分析的基准方法标注工具

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.307

Dominic Roberts, Mingzhu Wang, W. T. Calderon, M. Golparvar-Fard

引用次数: 7

Monitoring Bridge Degradation Using Dynamic Strain, Acoustic Emission and Environmental Data 利用动态应变、声发射和环境数据监测桥梁退化

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.17863/CAM.38694

Haris Alexakis, A. Franza, S. Acikgoz, M. DeJong

引用次数: 7

Optimal Sensor Placement Strategy for the Identification of Local Bolted Connection Failures in Steel Structures 钢结构螺栓连接局部失效识别的传感器优化布置策略

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.685

S. Biswal, Ying Wang

{"title":"Optimal Sensor Placement Strategy for the Identification of Local Bolted Connection Failures in Steel Structures","authors":"S. Biswal, Ying Wang","doi":"10.1680/ICSIC.64669.685","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.685","url":null,"abstract":"Failure of bolted connections in steel structures may result in catastrophic effects. Many algorithms in existing literature use \u0000modal information of a structure to identify damage in that structure, based on the data acquired from accelerometers which record the vibration \u0000time histories at different points on the structure. The location of these points may have significant effects on the quality of the acquired data, \u0000and thus the identified modal information. In this paper, a distance measure based Markov chain Monte Carlo algorithm is proposed to \u0000determine the optimal locations for the accelerometers, and the optimal location of the impact hammer if need. Different damage cases with \u0000various combinations of bolt failures are considered in this study. Failures at various levels are simulated by loosening the bolts in a predefined \u0000order. To compare the efficiency of the proposed method, the total effect of various damage cases on the accelerations at the optimal locations \u0000are calculated for the proposed method and a state-of-the-art method from the existing literature. The results demonstrate the efficiency of the \u0000proposed strategy in locating the accelerometers, which can produce data that are more sensitive to the bolted connection failures.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130913219","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}

引用次数: 5

Integration of Regional and Asset Satellite Observations for Assessment of Infrastructure Resilience 整合区域和资产卫星观测以评估基础设施复原力

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.029

K. Haria, M. Faragò, T. Dawood, M. Bush

引用次数: 1

Using Artifical Intelligence for Automating Pavement Condition Assessment 基于人工智能的路面状况自动评估

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.337

O. Aslan, E. Gultepe, Issa J. Ramaji, Sharareh Kermanshachi

{"title":"Using Artifical Intelligence for Automating Pavement Condition Assessment","authors":"O. Aslan, E. Gultepe, Issa J. Ramaji, Sharareh Kermanshachi","doi":"10.1680/ICSIC.64669.337","DOIUrl":"https://doi.org/10.1680/ICSIC.64669.337","url":null,"abstract":"The financial burden due to pavement damage on road networks is a major handicap to the economic development of a country. According to an ASCE report, this issue may cost as much as $67 billion per year. Regularly planned condition assessments and repairs of pavement can mitigate any derived costs and increase traffic safety. However, due to the large extent of civil infrastructure networks, required periodic inspections and assessments can be expensive and time-consuming. Further compounding the issue is that the majority of damage assessment mechanisms rely on human visual analysis, which can be prone to potential user bias and errors. In this study, we present a framework to automate roadway assessment by implementing a Convolutional Neural Network (CNN) that classifies various types of cracks in pavements. CNNs are a special type of deep artificial neural networks that demonstrate high accuracy and efficiency in image-based machine learning tasks. One of the main advantages of CNNs is that they can automatically learn the salient features of an image dataset without any prior knowledge or pre-processing by the user. Thus, the need for feature engineering is obviated and thereby eases the deployment of our assessment framework. Our framework was developed and tested on a balanced dataset containing 400 color images and consisting of four types of pavement damage: (1) longitudinal, (2) transverse, (3) alligator, and (4) pothole cracks. We apply image augmentation using a bundle of transformations to improve the crack classification accuracy of our CNN. The classification accuracy of the four types of cracks was found to be 76.2%. Demonstrating that the proposed CNN model can predict crack types without any user intervention at a good level of accuracy. To improve the robustness and accuracy of our assessment framework, we will analyze more types of cracks, using a larger dataset size in future studies.","PeriodicalId":205150,"journal":{"name":"International Conference on Smart Infrastructure and Construction 2019 (ICSIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652698","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}

引用次数: 5

Infrastructure Readiness for the Anticipated Transformative Changes in Transportation 为交通运输预期变革做好基础设施准备

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.481

Y. Huang, S. Jiang, M. Jafari, P. Jin

引用次数: 1

Photogrammetry and Augmented Reality for Underground Infrastructure Sensing, Mapping and Assessment 用于地下基础设施传感、测绘和评估的摄影测量和增强现实

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.169

M. Pereira, D. Orfeo, W. Ezequelle, D. Burns, Tian Xia, D. Huston

引用次数: 2

Monitoring of Shaft Excavations in Clay 粘土中立井开挖监测

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.655

Seda Sendir Torisu, N. Faustin, Mohammed Elshafie, M. Black, K. Soga, R. Mair

引用次数: 2

Monitoring Pumping Station Performance for Maintenance Optimisation 监察抽水站的表现，以达致最佳维修保养

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.649

O. Tarrant, K. Solts, S. Čarman, Y. Ugradar

引用次数: 0

A Framework of on-site Construction Safety Management Using Computer Vision and Real-Time Location System 基于计算机视觉和实时定位系统的现场施工安全管理框架

International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Pub Date : 2019-01-01 DOI: 10.1680/ICSIC.64669.327

Jinli Zhang, Dawei Zhang, Xin Liu, R. Liu, G. Zhong

引用次数: 9