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Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Computing in Civil Engineering 2017 Information Modeling and Data Analytics Selected Papers from the ASCE International Workshop on Computing in Civil Engineering 2017 Seattle, Washington June 25–27, 2017 Edited by Ken-Yu Lin, Ph.D.; Nora El-Gohary, Ph.D.; and Pingbo Tang, Ph.D., P.E. Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. COMPUTING IN CIVIL ENGINEERING 2017 INFORMATION MODELING AND DATA ANALYTICS SELECTED PAPERS FROM THE ASCE INTERNATIONAL WORKSHOP ON COMPUTING IN CIVIL ENGINEERING 2017 June 25–27, 2017 Seattle, Washington SPONSORED BY Computing Division of the American Society of Civil Engineers EDITED BY Ken-Yu Lin, Ph.D. Nora El-Gohary, Ph.D. Pingbo Tang, Ph.D., P.E. 1801 ALEXANDER BELL DRIVE RESTON, VIRGINIA 20191–4400 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Published by American Society of Civil Engineers 1801 Alexander Bell Drive Reston, Virginia, 20191-4382 www.asce.org/publications | ascelibrary.org Any statements expressed in these materials are those of the individual authors and do not necessarily represent the views of ASCE, which takes no responsibility for any statement made herein. No reference made in this publication to any specific method, product, process, or service constitutes or implies an endorsement, recommendation, or warranty thereof by ASCE. The materials are for general information only and do not represent a standard of ASCE, nor are they intended as a reference in purchase specifications, contracts, regulations, statutes, or any other legal document. ASCE makes no representation or warranty of any kind, whether express or implied, concerning the accuracy, completeness, suitability, or utility of any information, apparatus, product, or process discussed in this publication, and assumes no liability therefor. The information contained in these materials should not be used without first securing competent advice with respect to its suitability for any general or specific application. Anyone utilizing such information assumes all liability arising from such use, including but not limited to infringement of any patent or patents. ASCE and American Society of Civil Engineers—Registered in U.S. Patent and Trademark Office. Photocopies and permissions. Permission to photocopy or reproduce material from ASCE publications can be requested by sending an e-mail to [email protected] or by locating a title in ASCE's Civil Engineering Database (http://cedb.asce.org) or ASCE Library (http://ascelibrary.org) and using the “Permissions” link. Errata: Errata, if any, can be found at https://doi.org/10.1061/9780784480823 Copyright © 2017 by the American Society of Civil Engineers. All Rights Reserved. ISBN 978-0-7844-8082-3 (PDF) Manufactured in the United States of America. Computing in Civil Engineering 2017 iii Preface Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Welcome to Seattle, the Emerald City in Washington! The 2017 ASCE International Workshop on Computing in Civil Engineering (IWCCE) was held in Seattle from June 25-27, 2017. The workshop was hosted by the University of Washington with sponsorship from ASCE’s Computing Division. The workshop is the Computing Division’s major meeting event and is held biannually in the United States, with participation from scholars worldwide. The workshop has a long history of success and serves as a platform for sharing research innovation as well as valuable lessons. We introduced several pioneering changes this year, including the inaugural all-stakeholder meeting for the Computing Division. We had a strong and engaged Technical Committee which provided rigorous reviews for the abstracts and full papers, with each submission being reviewed by at least two members of our Technical Committee. The 2017 workshop, as a standalone event, received more than 300 abstracts, 184 full papers, and 32 extended abstracts for the poster and demonstration sessions. The participation from our growing community has set a record and a total of 162 full papers were accepted and included in the proceedings. Among these papers, Building Information Modeling and Civil Information Modeling formed the most popular technical interests while Energy, Sustainability and Resilience topped the list of application contexts. We would like to thank the Department of Construction Management at The University of Washington for its support of the workshop. We are also grateful for the guidance from the Computing Division’s Executive Committee and the assistance from ASCE. We hope that you enjoyed the technical sessions at the workshop and had a memorable and meaningful IWCCE experience in Seattle this year. Ken-Yu Lin, Ph.D. Chair, Organizing Committee, IWCCE 2017 Nora El-Gohary, Ph.D. Chair, Technical Committee, IWCCE 2017 Pingbo Tang, Ph.D., P.E. Vice Chair, Organizing Committee, IWCCE 2017 © ASCE Computing in Civil Engineering 2017 iv Acknowledgments Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Special thanks are due to the following individuals at the University of Washington for their continuous and tireless support throughout the organization of the workshop: Name Julie Angeley Mark Baratta Brian Vogt Zhenyu Zhang Title IWCCE Local Administrator IWCCE Local IT Lead IWCCE Local Web Consultant IWCCE Secretary A sincere appreciation goes to the Microsoft Corporation for providing the editors free access to Microsoft’s Academic Conference Management Service and for customizing the online platform for the workshop. The editors would also like to thank the following Technical Committee members for their assistance and effort with the paper review and selection process: Name Abbas Rashidi Albert Chen Ali Mostafavi Amin Hammad Amir Behzadan Andre Barbosa Andre Borrmann Atefeh Mohammadpour Auroop R. Ganguly Baabak Ashuri Behzad Esmaeili Bon-Gang Hwang Brenda McCabe Burcin Becerik Carl Haas Carlos Caldas Carol Menassa Changbum Ahn Chao Wang Chen Feng © ASCE Institution Georgia Southern University National Taiwan University Florida International University Concordia University Missouri State University Oregon State University The Technical University of Munich Indiana University-Purdue University Fort Wayne Northeastern University (United States) Georgia Institute of Technology University of Nebraska-Lincoln National University of Singapore University of Toronto University of Southern California University of Waterloo University of Texas at Austin University of Michigan University of Nebraska-Lincoln Louisiana State University Mitsubishi Electric Research Laboratories Computing in Civil Engineering 2017 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Chien-Cheng Chou Chimay Anumba Christian Koch David Lattanzi Dong Zhao Dulcy Abraham Ebrahim Karan Eduardo Santos Esin Ergen Fadi Castronovo Farrokh Jazizadeh Fei Dai Feng Li Fernanda Leite Frank Boukamp Frederic Bosche Guangbin Wang Hanbin Luo Hubo Cai Ian Smith Ioannis Brilakis Islam El-adaway Ivan Mutis Jack Cheng Jiansong Zhang Jiayu Chen Jie gong Jing Du Jinyue Zhang John Messner John Taylor Jun Yang Justin Ker-Wei Yeoh Koji Makanae Lu Zhang Lucio Soibelman Mani Golparvar-Fard Mario Berges Menghan Tsai Michael Olsen Ming Lu © ASCE v National Central University (Taiwan) University of Florida University of Nottingham George Mason University Michigan State University Purdue University Millersville University University of Sao Paulo Istanbul Technical University California State University East Bay Virginia Tech West Virginia University Research Institute of Highway (China) University of Texas at Austin Royal Melbourne Institute of Technology Heriot-Watt University Tongji University Huazhong University of Science and Technology Purdue University Ecole Polytechnique Federale (Switzerland) Cambridge University University of Tennessee Illinois Institute of Technology Hong Kong University of Science and Technology Western Michigan University City University of Hong Kong Rutgers University Texas A&M University Tianjin University Penn State University Georgia Tech Northwestern Polytechnical University (China) National University of Singapore Miyagi University Florida International University University of Southern California University of Illinois at Urbana-Champaign Carnegie Mellon University National Taiwan University Oregon State University University of Alberta Computing in Civil Engineering 2017 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Mounir El Asmar Nai-Wen Chi Nan Li Nipesh Pradhananga Nobuyoshi Yabuki Omar El-Anwar Oswald Chong Paul Goodrum Pin-Chao Liao Ray Issa Renate Fruchter Ren-Jye Dzeng Reza Akhavian Rishee Jain Robert Amor Rucheng Xiao Rui Liu Saiedeh Razavi Sanghoon Lee SangHyun Lee SangUk Han Semiha Ergan Seokho Chi Shang-Hsien Hsieh Sheryl Staub-French Steven Ayer Takashi Michikawa Tamer El-Diraby Timo Hartmann Walid Tizani Wen Xiong Xiangyu Wang Xianzhong Zhao Xiaowei Luo Xiaolong Xue Xuesong Liu Xuesong Shen Yelda Turkan Yimin Zhu Ying Zhou Yong Cho © ASCE vi Arizona State University National Taiwan University Tsinghua University Florida International University Osaka University Cairo University Arizona State University University of Colorado at Boulder Tsinghua University University of Florida Stanford University National Chiao-Tung University California State University East Bay Stanford University University of Auckland Tongji University University of Florida McMaster University University of Hong Kong University of Michigan University of Alberta New York University Seoul National University National Taiwan University University of British Columbia Arizona State University RIKEN University of Toronto Technical University of Berlin University of Nottingham Southeast University Curtin University Tongji University City University of Hong Kong Harbin Institute of Technology Carnegie Mellon University University of New South Wales Oregon State University Louisiana State University Huazhong University of Science and Technology Georgia Institute of Technology Computing in Civil Engineering 2017 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Youngjib Ham Yunfeng Chen Zhenhua Zhu Zheng Yang Zhiliang Ma vii Florida International University Georgia Southern University Concordia University Stanford University Tsinghua University Finally, the editors would also like to thank the following Poster and Demonstration Organization Committee members for their help with the related review process: Name Cheng Zhang Hamid Abdirad Jiawei Chen Kadir Amasyali Kaijian Liu Lufan Wang Luming Shang Vamsi Sai Kalasapudi Xuan Lv Zhenyu Zhang © ASCE Institution Arizona State University University of Washington Arizona State University University of Illinois at Urbana-Champaign University of Illinois at Urbana-Champaign University of Illinois at Urbana-Champaign University of Washington Arizona State University University of Illinois at Urbana-Champaign University of Washington Computing in Civil Engineering 2017 viii Contents Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Asset and Facility Management Optimizing Railroad Bridge Networks Management Using Mixed Integer Linear Programming and Genetic Algorithm ......................................................... 1 Amirhosein Jafari, Guillermo Pérez, Fernando Moreu, and Vanessa Valentin The Utilization of an Asset Safety Identification Tool (ASIT) to Support Safety during Facilities Management ...................................................... 10 Eric M. Wetzel, Jason Lucas, and Walid Y. Thabet Risk-Aware Multi-Objective Optimization of Capital Structure for Private Financing in Infrastructure Projects ......................................................... 18 Shuai Li and Hubo Cai Integration of BIM and Utility Sensor Data for Facilities Management ............. 26 Kamal Suprabhas and Hazar Nicholas Dib Unsupervised Recognition of Volumetric Structural Components from Building Point Clouds............................................................................................... 34 Jingdao Chen, Yihai Fang, and Yong K. Cho Defining a Taxonomy for Virtual 3D City Model Use Cases with a Focus on Facility Asset Management—A Virtual Campus Case Study .............. 43 Zhouqian Jiang, John I. Messner, and Craig R. Dubler BIM and CIM BIM to Facilities Management: Presenting a Proven Workflow for Information Exchange .............................................................................................. 51 Alireza Borhani, Hyun Woo Lee, Carrie Sturts Dossick, Laura Osburn, and Marc Kinsman Concepts for Formal Modeling and Management of Building Design Options ....................................................................................................................... 59 Hannah Mattern and Markus König Integrating BIM and Optimization Techniques for Enhanced Tower Crane Planning ......................................................................................................... 67 Yuanshen Ji, Bharathwaj Sankaran, Jiyong Choi, and Fernanda Leite © ASCE Computing in Civil Engineering 2017 Applied BIM: AMT and MTSP Integrated Approach for the Interior Patrol Routing Problem ............................................................................. 75 Chun-Hao Chen and Albert Y. Chen Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. A Semi-Automatic Approach to Detect Structural Components from CAD Drawings for Constructing As-Is BIM Objects ..................................................... 84 Qiuchen Lu and Sanghoon Lee Method of Bridge Structural Analysis Based on Bridge Information Modeling .................................................................................................................... 92 Rucheng Xiao, Yu Lian, Bin Sun, Xinwei Zhao, Zhao Liu, and Pingbo Tang Post-Earthquake Fire Simulations of Buildings Considering the Seismic Damage of Sprinkler Systems .................................................................. 101 Zhen Xu, Zongcai Zhang, and Xinzheng Lu A Framework for Rule-Based Validation of IFC Space Boundaries for Building Energy Analysis ....................................................................................... 110 Huaquan Ying and Sanghoon Lee 3D Model-Based Quantity Take-Off for Construction Estimates ...................... 118 Pengxiang Alex Han, Ming-Fung Francis Siu, Simaan AbouRizk, Di Hu, and Ulrich Hermann Model-Based Benchmarking for Healthcare Projects: System Requirements and Demonstration ........................................................... 125 Jiyong Choi, Fernanda Leite, and Daniel P. de Oliveira Linking BIM and GIS Models in Infrastructure by Example of IFC and CityGML .............................................................................. 133 S. Vilgertshofer, J. Amann, B. Willenborg, A. Borrmann, and T. H. Kolbe Automated Wood Construction Cost Estimation ................................................ 141 Temitope Akanbi and Jiansong Zhang Investigating Building Sustainability by Applying Sensitivity Analysis of Impact Factors during Design Stage ................................................. 149 C. Zhang and L. Ong Refinement of the Visual Code Checking Language for an Automated Checking of Building Information Models Regarding Applicable Regulations .............................................................................................................. 157 Cornelius Preidel and André Borrmann © ASCE ix Computing in Civil Engineering 2017 Semantic-Rich 3D CAD Models for Built Environments from Point Clouds: An End-to-End Procedure ............................................................. 166 Yeritza Perez-Perez, Mani Golparvar-Fard, and Khaled El-Rayes Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Investigation of Leveraging BIM Standards to Facilitate Sustainability Evaluations from Early Stages of Design ............................................................. 175 Issa J. Ramaji, Pelin Gultekin-Bicer, Raphael W. Crowley, and J. David Lambert Improving RSSI-Based Indoor Localization Performance by Integrating BIM ...................................................................................................... 184 Hainan Chen, Xiaowei Luo, and Jian Guo Framework of Dynamic Daily 4D BIM for Tracking Construction Progress through a Web Environment ................................................................. 193 Jaehyun Park and Hubo Cai Benefits of Real-Time Data Driven BIM for FM Departments in Operations Control and Maintenance .................................................................. 202 Omid Davtalab Exploring the Body of Knowledge for Building Information Modeling Implementation Using the Delphi Method ........................................................... 211 Wei Wu, Glenda Mayo, Raja R. Issa, Tammy McCuen, and Deke Smith A Comprehensive Identification and Categorisation of Drivers, Factors, and Determinants for BIM Adoption: A Systematic Literature Review ...................................................................................................................... 220 Ahmed L. Ahmed, John P. Kawalek, and Mohamad Kassem A Case Study in Data Visualization for Linked Building Information Model and Building Management System Data .................................................. 228 Jennifer I. Lather, Robert Amor, and John I. Messner An Automated Reconstruction Approach of Mechanical Systems in Building Information Modeling (BIM) Using 2D Drawings ............................... 236 Chi Yon Cho and Xuesong Liu BIM-Integrated System for Automated Value Analysis of Buildings................ 245 Lu Zhang and Nora M. El-Gohary Keyword-Driven Model View Generation for Civil Infrastructure Projects .................................................................................................................... 254 Tuyen Le and H. David Jeong © ASCE x Computing in Civil Engineering 2017 xi BIM-Based Construction Noise Hazard Prediction and Visualization for Occupational Safety and Health Awareness Improvement ................................ 262 Weile Wei, Chao Wang, and Yongcheol Lee Data Analytics Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Sentiment Analysis for the Construction Industry: A Case Study of Weibo in China ....................................................................................................... 270 L. Y. N. Tang, Y. M. Zhang, F. Dai, Y. J. Yoon, and Y. Q. Song Using Data Driven Methodologies to Identify Patterns in BAS Data to Support Facility Operations ............................................................ 282 Gokmen Dedemen, Marjan Vakilinezhad, and Semiha Ergan Towards Automated Inference of Occupant Behavioral Dynamics Using Plug-Load Energy Data .......................................................................................... 290 Andrew J. Sonta, Perry E. Simmons, and Rishee K. Jain Deep Active Learning for Civil Infrastructure Defect Detection and Classification ........................................................................................................... 298 Chen Feng, Ming-Yu Liu, Chieh-Chi Kao, and Teng-Yok Lee Automated Recognition and Localization of Parking Signs Using Street-Level Imagery .............................................................................................. 307 Qazaleh Mirsharif, Théophile Dalens, Mehdi Sqalli, and Vahid Balali Similarity-Based Dependency Parsing for Extracting Dependency Relations from Bridge Inspection Reports ........................................................... 316 Kaijian Liu and Nora El-Gohary Data-Driven Residential Building Energy Consumption Prediction for Supporting Multiscale Sustainability Assessment ............................................... 324 Lufan Wang and Nora M. El-Gohary Stakeholder Opinion Classification for Supporting Large-Scale Transportation Project Decision Making ............................................................. 333 Xuan Lv and Nora M. El-Gohary Predicting Leaks in Natural Gas Distribution Networks Using Generalized Linear Models .................................................................................... 342 Yasamin H. Tari, Burcu Akinci, Mario Bergés, and Matteo Pozzi Building Energy Use Modes and Thermal Comfort ............................................ 350 Kadir Amasyali and Nora El-Gohary © ASCE Computing in Civil Engineering 2017 Infrastructure Monitoring, Control, and Analysis Level-of-Expertise Classification for Identifying Safe and Productive Masons ..................................................................................................................... 359 Abdullatif Alwasel, Mohammad Nahangi, Carl Haas, and Eihab Abdel-Rahman Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. 3D Approach for Representing Uncertainties of Underground Utility Data .... 369 L. L. olde Scholtenhuis, S. Zlatanova, and X. den Duijn Geometry-Based Optimized Point Cloud Compression Methodology for Construction and Infrastructure Management ................................................... 377 Jiawei Chen, Cheng Zhang, and Pingbo Tang 3D Thermal and Spatial Modeling of a Subway Tunnel: A Case Study ........... 386 Ghassan Al Lafi, Zhenhua Zhu, Thikra Dawood, and Tarek Zayed Representation Requirements for Laser Scan Based Probabilistic Condition Assessment of Bridges .......................................................................... 395 Varun Kasireddy and Burcu Akinci Tracking Structural Deformations via Automated Sample-Based Point Cloud Analysis ........................................................................................................ 403 Bahman Jafari, Ali Khaloo, and David Lattanzi An Automatic Robust Point Cloud Registration on Construction Sites ............ 411 Pileun Kim and Yong K. Cho An Autonomous Video Analysis Method for Crack Detection on Metallic Surfaces Based on Texture Recognition and Bayesian Data Fusion .................. 420 Fu-Chen Chen, Mohammad R. Jahanshahi, Rih-Teng Wu, and Chris Joffe A Preliminary Study on Disaster Waste Detection and Volume Estimation Based on 3D Spatial Information ...................................................... 428 Hyung Taeck Yoo, Hyunwoo Lee, Seokho Chi, Bon-Gang Hwang, and Jinwoo Kim Smart City and Transportation Systems EMS Response Actions in Mass Casualty Incidents ............................................ 436 Chang-Chi Chou and Albert Y. Chen Video-Based Indoor Human Detection for Decision-Making of the Installation Locations for Automated External Defibrillators ........................... 444 Ching-Chun Chen and Albert Y. Chen Partition Problem for Optimizing the Deployment of Incident Response ........ 450 Zhi-You Dai, Jhihfu Kang, Ning Li, Likai Yang, and Yu-Ting Hsu © ASCE xii Computing in Civil Engineering 2017 Building Smart Transportation Hubs with Internet of Things to Improve Services to People with Disabilities........................................................ 458 Jie Gong, Cecilia Feeley, Hao Tang, Greg Olmschenk, Vishnu Nair, Zixiang Zhou, Yi Yu, Ken Yamamoto, and Zhigang Zhu Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. A Data Integration Framework for Urban Systems Analysis Based on Geo-Relationship Learning .................................................................................... 467 Zheng Yang, Karan Gupta, Archana Gupta, and Rishee K. Jain © ASCE xiii Computing in Civil Engineering 2017 1 Optimizing Railroad Bridge Networks Management Using Mixed Integer Linear Programming and Genetic Algorithm Amirhosein Jafari1; Guillermo Pérez2; Fernando Moreu3; and Vanessa Valentin4 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. 1 Dept. of Civil Engineering, Univ. of New Mexico, 210 University Blvd NE, Albuquerque, 87106. E-mail: [email protected] 2 Dept. of Civil Engineering, Univ. of New Mexico, 210 University Blvd NE, Albuquerque, 87106. E-mail: [email protected] 3 Dept. of Civil Engineering, Univ. of New Mexico, 210 University Blvd NE, Albuquerque, 87106. E-mail: [email protected] 4 Dept. of Civil Engineering, Univ. of New Mexico, 210 University Blvd NE, Albuquerque, 87106. E-mail: [email protected] NM NM NM NM Abstract Railroad management entities in the U.S. are developing new tools to improve the management of railroad bridge networks, in order to comply with new federal regulations on bridge safety and to increase their profitability. Decisions about maintenance, repair, and replacement (MRR) actions are currently prioritized by rating the bridges based on structural inspections and predictions about the estimated costs of operations. This study proposes a framework for the management of railroad bridge networks that: (1) utilizes a consequence-based management approach that considers relationships between displacements, serviceability levels and bridge MRR decisions; and (2) minimizes the expected value of total network costs by determining the best MRR decisions based on an annual MRR budget. Through this study, two different optimization methods are explored in two different scenarios: (I) mixed integer linear programming (MILP) when the impact of bridge location on costs is insignificant resulting in linear objective function and constraints; and (II) genetic algorithm (GA) when the impact of bridge location on costs is significant resulting in nonlinear objective function and constraints. A case study of a network comprised of 100 railroad bridges is used to demonstrate the proposed framework. The results show that scenario I leads the optimum MRR decision to replace more bridges. On the other hand, scenario II leads the optimum MRR decision to more repair or maintain groups of bridges which are closer to each other. INTRODUCTION Railroads deteriorate naturally over time mainly due to usage, environmental effects, and aging (Mishalani and Gong 2009). On the other hand, railroads in U.S. expect to exceed their capacities over the next 20 years at many locations within their network. Bridges are a critical component of railroad infrastructure, as researchers use the term ‘bridge network’ for the transportation network recognizing that the bridge is the most fragile component in the entire system (Bocchini and Frangopol 2011). Railroad entities in the U.S. are developing new tools to improve the management of railroad bridge networks, in order to comply with new federal regulations on bridge safety and to increase their profitability. An important aspect for improving capacity of network bridges is annual implementation of maintenance, repair and replacement (MRR) actions. Railroad managers invest in MRR actions every year to allow the operations of traffic at the required speeds while guaranteeing safe operations. The available financial resources, however, are insufficient to implement MRR actions for all of U.S. bridge network. © ASCE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Computing in Civil Engineering 2017 Therefore, a MRR management system of a bridge network is required for cost-effective allocation of these limited funds and consequently prioritizing MRR strategies for the bridge network. To determine the implementation of MRR actions to railroad bridges in a network, railroads use bridge inspection reports (American Railway Engineering and Maintenance-of-Way Association 2014). These inspections are regularly made to assess the facility condition and to forecast its deterioration. Based on inspection outcomes, MRR actions are performed to reduce deterioration (Mishalani and Gong 2009). One challenge regarding the existing MRR management systems based on inspections is that decisions made on the basis of infrastructure condition states alone may cause serious safety consequences (Frangopol and Liu 2007). Using these inspections, the current strategy is choosing bridges with more urgent work plans first. Another challenge regarding the existing MRR management systems is that decisions are not necessarily costeffective. MRR management of bridges network can be formulated as a combinatorial optimization problem because there always exist objectives to be optimized (Frangopol and Liu 2007). This optimization problem can be defined either as a single- or multi-objective problem. The most widely used objective for MRR planning problems is minimizing the present value of life-cycle costs while satisfying constraints imposed on important bridge performance measures such as structural reliability (Liu et al. 1997). More recently, multiple and conflicting performance indicators (e.g., condition, safety, and durability) along with life-cycle cost have also been simultaneously considered as separate objective functions in the formulation of bridge maintenance planning problems (Frangopol and Liu 2007; Liu et al. 1997; Liu and Frangopol 2005). In addition to the aforementioned challenges, the number of MRR strategies may be large posing problems that are computationally intensive. If a railroad network has n bridges with 2 options of (1) performing or (2) not performing MRR over t years, then the total number of alternative strategies is 2n × t. For long-term decision making, there will be a large number of alternative strategies. Besides, the process of decision-making for MRR plan is a complex procedure because the problem contains many inherent interactions among management objectives, system elements and planning period. In order to overcome these challenges, a bridges network MRR management system is required to: (1) assess and forecast the bridge condition considering tools that go beyond regular inspections, (2) select the most cost-effective MRR strategy for the network, and (3) conduct the decision-making process in a reasonable time. This study proposes a framework for the management of railroad bridge networks that: (1) utilizes a consequence-based management approach that considers relationships between displacements, serviceability levels and bridge MRR decisions; and (2) minimizes the expected value of total network costs by determining the best MRR decisions based on an annual MRR budget. Through this study, two different optimization methods are explored in two different scenarios: (I) mixed integer linear programming (MILP) when the impact of bridge location on costs is insignificant resulting in linear objective function and constraints; and (II) genetic algorithm (GA) when the impact of bridge location on costs is significant resulting in nonlinear objective function and constraints. A case study of a network comprised of 100 railroad bridges is used to demonstrate the proposed framework. © ASCE 2 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Computing in Civil Engineering 2017 OPTIMIZATION TECHNIQUES Several prior research have used optimization techniques for the management of civil infrastructure (Liu et al. 1997). Single or multiple performance indicators such as infrastructure condition, safety and durability along with life-cycle cost are simultaneously considered as objectives on previous researches (Chen et al. 2015; Hsieh and Liu 2004; Liu and Frangopol 2005; Mishalani and Gong 2009). To tackle the difficult task of finding the best solution for decision-making in infrastructures network, optimization tools play an important role in addressing this type of problem. Since infrastructure investment decision is essentially a 0-1, nonlinear, and constrained problem (Hsieh and Liu 2004), most of previous research efforts use GA as numerical optimization tools to solve the problem (Chen et al. 2015; Frangopol and Liu 2007; Liu et al. 1997; Liu and Frangopol 2005). However, by simplifying the problem to linear equations, a MILP could be used (Chen et al. 2015; Jacobs 1992). This study uses both MILP and GA for optimization methods. MILP. Refers to mathematical programming with continuous and discrete variables and linearity in the objective function and constraints. The use of MILP is a natural approach of formulating problems where it is necessary to simultaneously optimize the system structure (discrete) and parameters (continuous) (Bussieck and Pruessner 2003). GA. Genetic algorithms, as one type of heuristic algorithms, are stochastic searches and optimization tools that follow the survival-of-the-fittest principle from the biological sciences (Goldberg 1989). Since they formally appeared in the 1960s, GAs have been successfully applied to a wide range of problems because of their ease of implementation and robust performance (Frangopol and Liu 2007; Jafari and Valentin 2015). GA is a method for solving both constrained and unconstrained optimization problems. FRAMEWORK DEVELOPMENT The developed framework in this study utilizes five main steps: (1) describing the required components to assess and forecast the bridge condition; (2) inspection to identify the according MRR actions; (3) calculating network life-cycle costs; (4) formulating the model for optimization; and (5) using optimization tools find the best MRR strategies. These steps are described as follow: Step 1. Model Components Three components are used to establish the relationship between displacements, serviceability and bridge MRR actions for each bridge in the railroad network. The first component is the hazard, assumed as the maximum total transverse displacement of bridges in the inventory, measured under a loaded train running at the maximum allowable speed for their track class. The second component are fragility curves elaborated assuming that network bridges have similar structural properties and that the serviceability of each bridge is independent from the bridge location within the network. The third component is monitoring that senses the maximum measurement of transverse displacement for a given bridge under train load, assuming this maximum displacement represents the bridge condition. These three components are used to estimate operational costs of bridges based on displacement. For more information about the integration of aforementioned components, see Moreu et al. (2017). © ASCE 3 4 Step 2. Inventory The second step of the framework is defining the bridge inventory, which corresponds here to the population of bridges owned by the specific railroad for which the MRR policies are being developed, and their current structural condition. The bridges in the inventory have already been identified by the railroad to need MRR decisions. This framework assumes that the bridges being monitored share similar structural properties and operational concerns, so that the measurement of the hazard of different components of the inventory can be used for relative comparisons within the inventory. Step 3. Cost Calculation The cost to the railroad to maintain the bridge network consists of two components: (i) the MRR costs for the bridge network, and (ii) the operational costs (OC). OC include the operational expense (OE), or the expense beyond MRR investments to maintain the bridge network to meet operational needs, and the lost revenue (LR) to the railroad associated with not doing MRR on specific bridges. Railroads decrease the speed of trains over bridges of poor condition, assuming the associated expenses related to traffic delay and decreased network efficiency (Moreu et al. 2017). The total network cost (TNC) is the cost of MRR, plus the OC. Figure 1 shows the relationship between MRR investments and the TNC associated with maintaining the bridge network over a specified period of time. Low investments of MRR are associated with high expenses (OE), i.e. if the bridge network is poorly maintained the cost to operate it will be higher, whereas large MRR investments increase the bridge condition and reduce the expenses associated to poor bridge performance (Moreu et al. 2017). The goal is to choose MRR policies that will minimize the expected value of the TNC. MRR Costs. The inspection reports recommend different MRR decisions that are based on structural capacity. The costs of these MRR actions are deterministic and can be calculated based on the length of the bridge (Moreu et al. 2017). Operational Costs. The operational costs of bridge per year can be calculated assuming OEs of unplanned engineering work provided by the railroad as well as LR related to delay or interruptions to traffic. It is assumed that the operational costs related to the conditions of the bridge are the only variables in the decision-making, neglecting other factors including, but not limited to: access to the bridge, financial decisions related to strategic planning of operations, proximity of related railroad operations to the bridge, among others. TNC Optimum MRR Cost Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Computing in Civil Engineering 2017 OC MRR Annual MRR budget Figure 1. OC and MRR Cost Relations (Moreu et al. 2017) Step 4. Model Formulation Before applying optimization, a decision-making problem is formulated mathematically while ensuring that its goals and requirements are represented accurately. Binary variables are used as © ASCE Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Computing in Civil Engineering 2017 decision variables = ( , , … , ) representing the selection of strategies (n is number of bridges), as shown in Eq. (1): 1 = (1) 0 The total MRR cost for the bridge network is calculated as shown in Eq. (2): = ∑ × − (2) Where is the MRR costs of ith bridge and is the premium to the cost of MRR based on the proximity between bridges. The assumption for is as follows: • $100K: for bridges within 1 mile they have a 100K bonus. This assumes that for example, if two bridges to be repaired are within a mile of each other, equipment mobilization expenses will be reduced. • $50K: for bridges within 5 miles, other partial benefits for sharing some of the equipment The total OC of the bridges network is calculated as shown in Eq. (3): = ∑ × × (1 − ) − (3) th is the operational costs of i bridge and is premium of OC in bridges Where which are close to each other, to avoid penalizing multiple times for slow down the train speed in a short distance. The assumption for is as follows: • 25% of OC for each bridge if there is another bridge with no MRR action within 5 miles from one side of a bridge • 50% of OC for each bridge if there are other bridges with no MRR action within 5 miles from both sides of a bridge In addition, is defined as operational cost reduction factor for the first year which considers that maintenance or repair actions may reduce OC due to improved safety. The value of for replacement is 100%, which assumes that replacing a bridge will eliminate all of its OC in the first year. The value of in this study is 100% for replacement, 1% for repair, and ≈0% for maintenance. Assuming that the MRR decision should be made based on annual MRR budget, the problem can be formulized as minimizing OC of the network from Eq. (3), subject to having MRR costs from Eq. (2) less than or equal to annual MRR budget. The problem can also be applied to multiple year scenarios (short term). An interest rate of 6% is applied following Moreu et al. (Moreu et al. 2017). Step 5. Optimization Methods Two different scenarios are explored in this study: I. When the impact of bridge location on costs is insignificant ( = ≈ 0) which results in linear objective and constraints and requires MILP to find the solution; II. When the impact of bridge location on costs is significant which results in nonlinear objective and constraints and requires GA to find the solution. MILP is able to find the best solution quickly, however it requires an assumption of eliminating impact of location of bridges network on the costs. To consider the realistic impact of the location of bridges, more time is required to find an optimum solution using GA. © ASCE 5 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Computing in Civil Engineering 2017 DATA COLLECTION A case study of a network comprised of 100 railroad bridges is used to demonstrate the framework. The data consists of recent timber railroad bridge replacements in the Midwest, but their specific properties are modified to maintain confidentiality of the company providing this information. All bridges are in the main route (high traffic), but different traffic levels are shown for different bridges. The inspection reports recommend different MRR decisions that are included in the data set. MRR decisions are based on structural capacity and MRR costs are calculated based on the length of the bridge. MRR costs for each bridge are independent of the service condition of the bridge. Among these 100 bridges, 30 bridges are assigned for replacement, 50 bridges are assigned for repair, and 20 bridges are assigned for maintenance. Without budgetary limit, the railroad would implement all recommended MRR actions to the entire population of timber bridges to increase the capital, the capacity, the efficiency and the safety of their railroad network, since replacing timber railroad bridges eliminate risks and increase capacities. However, the MRR cost of all 100 bridges is $190.3M which is neither affordable nor urgently needed. RESULTS AND DISSCUSSION MRR policies can be modified for a fixed annual MRR budget and minimizing operational costs. For three consecutive years, the different MRR and operational costs values are calculated and added together for a network of 100 bridges of similar properties as those described in previous section. It is assumed that the bridges that are replaced will not have any operational costs on the current year and future years. Therefore, the bridges which are replaced in year n will be removed from the decision space from year n+1 and next years. For bridges that are repaired, the operational cost (calculated by in Eq. 3) is decreased in the current year but will have operational costs after that if no additional repair or maintenance action is implemented. The scenarios described in Step 5 of the methodology section are described below. First Scenario – MILP (I): To find the best amount of annual MRR budget for the case study (optimum point in Figure 1), the simulation is run for different annual MRR budgets of $1M to $15M with intervals of $1M. The results are shown in Figure 2a. In this case, the optimum amount of annual MRR budget for three consequence years is between $3M to $4M. In addition, the model is run for a $10M annual MRR budget for a 3 –year consequence management of the bridge network. The optimum MRR decisions for three years are shown in Figure 3a. In this case, a total of 6, 1, and 2 bridges are selected for replacement for the first, second, and third year, respectively. Therefore, 9 out of 30 bridges will be replaced by annual MRR budget of $10M based on the optimum decision. Second Scenario – GA (II): Similarly, to find out the best amount of annual MRR budget, the simulation is run for different annual MRR budgets of $1M to $15M with the intervals of $1M. The results are shown in Figure 2b. In this case, the optimum amount of annual MRR budget for three consequence years is between $3M to $5M. In addition, the model is run for a $10M annual MRR budget for a 3-year consequence management of the bridge network. The optimum MRR decision for three years are shown in Figure 3b. As it is shown, a total of 2, 1, and 0 bridges are selected for replacement for first, second, and third year, respectively. Therefore, only 3 out of 30 bridges will be replaced by annual MRR budget of $10M based on the optimum decision. © ASCE 6
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