Tài liệu Computing in civil engineering 2017 smart safety, sustainability, and resilience

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 Smart Safety, Sustainability, and Resilience 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 SMART SAFETY, SUSTAINABILITY, AND RESILIENCE 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/10.1061/9780784480847 Copyright © 2017 by the American Society of Civil Engineers. All Rights Reserved. ISBN 978-0-7844-8084-7 (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. 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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. 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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. Computing for Human Factors Post-Simulation Visualization of Construction Manual Operations Using Motion Capture Data ....................................................................................... 1 Alireza Golabchi, SangUk Han, and Simaan AbouRizk The Impact of Engineering Information Formats on Craft Worker Eye Gaze Patterns .............................................................................................................. 9 Omar F. Alruwaythi, Matthew H. Sears, and Paul M. Goodrum Towards an Occupancy-Enhanced Building HVAC Control Strategy Using Wi-Fi Probe Request Information .......................................................................... 17 Xuan Li, Xuesong Liu, and Zhen Qian A Real-Time Emergency Evacuation Management System (REEMS) Using Indoor Localization Technology .............................................................................. 25 Faxi Yuan and Rui Liu Construction Management Investigating the Performance of Relational Contracts Using Social Network Analysis ...................................................................................................... 34 Mahmoud M. Abd El-Moneim, Omar H. El-Anwar, and Islam H. El-Adaway Performance Analysis of a Probabilistic Local Search Algorithm for Indoor Tracking ........................................................................................................ 43 JeeWoong Park and Yong K. Cho BIM-Based Life Cycle Assessment and Costing of Buildings: Current Trends and Opportunities ........................................................................................ 51 M. N. Nwodo, C. J. Anumba, and S. Asadi Construction Method Models Using Context Aware Construction Requirements for Automated Schedule Generation ............................................. 60 Justin K. W. Yeoh, T. Q. Nguyen, and Ernest L. S. Abbott Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models ........................................................................................ 68 P. Racz, L. Syfuss, C. Schultz, M. van Buiten, L. olde Scholtenhuis, F. Vahdatikhaki, and A. Dorée © ASCE Computing in Civil Engineering 2017 ix Mixed Reality for Electrical Prefabrication Tasks ................................................ 76 Jad Chalhoub and Steven K. Ayer Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Long Term Change Pattern Analysis of Bidding Price Based on Non-Parametric Framework ................................................................................... 84 Yang Cao and Baabak Ashuri Construction Work Plan Prediction for Facility Management Using Text Mining ........................................................................................................................ 92 Yunjeong Mo, Dong Zhao, Matt Syal, and Azizan Aziz Towards Developing an Ontology for Earthwork Operations ........................... 101 Alhusain Taher, Faridaddin Vahdatikhaki, and Amin Hammad Model-Driven Visual Data Capture on Construction Sites: Method and Metrics of Success ............................................................................. 109 Amir Ibrahim, Mani Golparvar-Fard, Timothy Bretl, and Khaled El-Rayes An Interactive Model-Driven Path Planning and Data Capture System for Camera-Equipped Aerial Robots on Construction Sites .................................... 117 Amir Ibrahim, Dominic Roberts, Mani Golparvar-Fard, and Timothy Bretl Developing a Satisfactory Input for Project Complexity Model Using Principal Component Analysis (PCA) .................................................................. 125 Bac Dao, Stuart Anderson, and Behzad Esmaeili Single Tower Crane Allocation Using Ant Colony Optimization ...................... 132 Carlos Trevino and Mohamed Abdel-Raheem Potential of Convolutional Neural Network-Based 2D Human Pose Estimation for On-Site Activity Analysis of Construction Workers.................. 141 Meiyin Liu, SangUk Han, and SangHyun Lee Error Quantification and Visualization in Using Sensors to Position Backhoe Excavator ................................................................................................. 150 Monjurul Hasan and Ming Lu Measuring the Impact of Working Memory Load on the Safety Performance of Construction Workers ................................................................ 158 Sogand Hasanzadeh, Bac Dao, Behzad Esmaeili, and Michael D. Dodd Crowdsourcing and Citizen Science Visual Awareness on Surface Flow Measurement ............................................... 167 Yao-Yu Yang and Shih-Chung Kang © ASCE Computing in Civil Engineering 2017 x Imagery-Based Risk Assessment Using Crowdsourcing Technology in Complex Workspaces ............................................................................................. 174 Cheng Zhang, Pingbo Tang, Pin-Chao Liao, and Yi Ren Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Effects of Physical Disorders in Neighborhoods on Pedestrians’ Physiological Responses ......................................................................................... 183 Mohammad Bisadi, Hyunsoo Kim, Changbum R. Ahn, and Yunwoo Nam Energy, Sustainability, and Resilience Analysis of Delay Interval and Energy-Load Variation for Non-Intrusively Extracting Occupant Energy-Use Information in Commercial Buildings ........ 191 Hamed Nabizadeh Rafsanjani, Changbum Ahn, and Jiayu Chen Modelling the Dynamic Interaction between Building Performance and Occupant Well-Being.............................................................................................. 198 Flavia Grey and Renate Fruchter An Occupant-Centered Integrated Lighting and Shading Control for Energy Saving and Individual Preferences .......................................................... 207 Ting-Chun Kuo, Ying-Chieh Chan, and Albert Y. Chen Stochastic Optimization Model for Sustainable Water Treatment with Minimal Energy Use ............................................................................................... 215 Jacob Blackwood, Ebrahim Karan, Somayeh Asadi, Atefeh Mohammadpour, and Sadegh Asgari Probabilistic Building Energy Performance Analysis of Ultra-High-Performance Fiber-Reinforced Concrete (UHP-FRC) Façade System ......................................................................................................... 223 S. M. Shahandashti, B. Abediniangerabi, B. Bell, and S. H. Chao Identifying Critical Links in Water Supply Systems Subject to Various Earthquakes to Support Inspection and Renewal Decision Making ................. 231 B. Pudasaini, S. M. Shahandashti, and M. Razavi Data Sensing Approaches to Monitoring Building Energy Use and Occupant Behavior ................................................................................................. 239 Yewande S. Abraham, Chimay J. Anumba, and Somayeh Asadi Big Buildings and Big Data: Do Energy Disclosure Policies Impact Energy Use over Time? ........................................................................................................ 248 Sokratis Papadopoulos and Constantine E. Kontokosta © ASCE Computing in Civil Engineering 2017 xi Emergence of Resilience from Infrastructure Dynamics: A Simulation Framework for Theory Building ................................................... 256 Kambiz Rasoulkhani, Maria Presa Reyes, and Ali Mostafavi Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Resilience Strategies for Interdependent Multiscale Lifeline Infrastructure Networks .................................................................................................................. 265 Lina Sela, Udit Bhatia, Janice Zhuang, and Auroop Ganguly Latent Relationship between Construction Cost and Energy Efficiency in Multifamily Green Buildings ................................................................................. 273 Yunjeong Mo, Dong Zhao, Andrew McCoy, Jing Du, and Philip Agee Spatially Constrained Decentralization of Urban Energy Supply Driven by Fluctuations in Human Mobility ........................................................................... 281 Neda Mohammadi and John E. Taylor Simulation-Based Optimization of Building Renovation Considering Energy Consumption and Life-Cycle Assessment ............................................... 288 Seyed Amirhosain Sharif and Amin Hammad An Ontology to Support the Move towards Sustainable Construction in Saudi Arabia............................................................................................................ 296 Abdulmajeed Howsawi and Jiansong Zhang Mapping Wind Energy Potential in New York City Using Geographic Information System Platform and Semi-Analytical Methods ............................ 304 Samaneh Gholitabar and Fletcher H. Griffis Non-Intrusive Detection of Respiration for Smart Control of HVAC System ...................................................................................................................... 310 Wooyoung Jung and Farrokh Jazizadeh Multi-Scale Modeling of a 500-Year CSZ Tsunami Inundation with Constructed Environment...................................................................................... 318 Xinsheng Qin, Michael R. Motley, Randall J. LeVeque, and Frank I. Gonzalez Project Planning and Control Sound Recognition Techniques for Multi-Layered Construction Activities and Events .............................................................................................. 326 Chunhee Cho, Yong-Cheol Lee, and Tianyi Zhang Architectural Freedom in Spite of Precast Elements: An Integrated Optimization Approach.......................................................................................... 335 K. Klemt-Albert, P. Hagedorn, and T. Pullmann © ASCE Computing in Civil Engineering 2017 xii Integrating Highway Projects Data in GIS for Maintenance and Rehabilitation Planning: Applications, Challenges, and Recommendations .... 343 Jojo France-Mensah, Bharathwaj Sankaran, and William J. O’Brien Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Acoustical Modeling of Construction Jobsites: Hardware and Software Requirements .......................................................................................................... 352 C. F. Cheng, A. Rashidi, M. A. Davenport, D. V. Anderson, and C. A. Sabillon AEC Project Performance Prediction and Validation Using the Artificial Neural Network ....................................................................................................... 360 Ivan Leung, Min Song, and Calvin Kam Sensor-Based Resource Tracking for Monitoring the Progress of Rebar Installation ............................................................................................................... 368 Gursans Guven and Esin Ergen Automation of Project Planning and Resource Scheduling on a Rough Grading Project ...................................................................................................... 376 D. Li, C. Yi, and M. Lu Smart Safety and Health A Comprehensive Spatio-Temporal Network-Based Model for Dynamic Risk Analysis on Struck-by-Equipment Hazard ................................................. 384 Jun Wang and Saiedeh Razavi Feasibility of a Drone-Based On-Site Proximity Detection in an Outdoor Construction Site ..................................................................................... 392 D. Kim, K. Yin, M. Liu, S. Lee, and V. R. Kamat Gazetteers for Information Extraction Applications in Construction Safety Management ............................................................................................................ 401 Nai-Wen Chi, Ken-Yu Lin, Nora El-Gohary, and Shang-Hsien Hsieh An Ensemble Approach for Classification of Accident Narratives.................... 409 C. U. Ubeynarayana and Y. M. Goh Investigation of the Relationship between Construction Workers’ Psychological States and Their Unsafe Behaviors Using Virtual Environment-Based Testing .................................................................................. 417 Yantao Yu, Jiansong Zhang, and Hongling Guo Enhancing Motion Trajectory Prediction for Site Safety by Incorporating Attitude toward Risk ..................................................................... 425 Khandakar M. Rashid and Amir H. Behzadan © ASCE Computing in Civil Engineering 2017 Construction Productivity and Ergonomic Assessment Using Mobile Sensors and Machine Learning ............................................................................. 434 Nipun D. Nath and Amir H. Behzadan Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Detecting and Classifying Cranes Using Camera-Equipped UAVs for Monitoring Crane-Related Safety Hazards.......................................................... 442 Dominic Roberts, Timothy Bretl, and Mani Golparvar-Fard Joint Reasoning of Visual and Text Data for Safety Hazard Recognition ........ 450 Shuai Tang and Mani Golparvar-Fard Analyzing Spatial Patterns of Workers’ Gait Cycles for Locating Latent Fall Hazards ............................................................................................................ 458 Kanghyeok Yang, Changbum Ahn, Mehmet C. Vuran, and Hyunsoo Kim © ASCE xiii Computing in Civil Engineering 2017 Post-Simulation Visualization of Construction Manual Operations Using Motion Capture Data Alireza Golabchi1; SangUk Han2; and Simaan AbouRizk3 1 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Univ. of Alberta, 9211 116th St., Edmonton, AB, Canada T6G 1H9. E-mail: [email protected] 2 Assistant Professor, Dept. of Civil and Environmental Engineering, Hanyang Univ., 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Korea. E-mail: [email protected] 3 Professor, Dept. of Civil and Environmental Engineering, Univ. of Alberta, 9211 116th St., Edmonton, AB, Canada T6G 1H9. E-mail: [email protected] Abstract Considering the significant proportion of manual operations in the construction industry, effective monitoring and control of manual activities through modeling and visualization can play a critical role in improving productivity and safety of projects. Furthermore, using motion capture data to visualize worker movements, as part of the virtual representation of a workplace, can be highly beneficial for applications pertaining to analysis of human motions. Thus, this study proposes a post-simulation visualization framework, which uses automated creation of motion capture data, to provide a reliable and realistic representation of manual operations. The approach has been implemented on a manual task, and the results confirm its effectiveness in enabling an easy to use modeling of manual activities for various visualization applications. INTRODUCTION Simulation modeling is a powerful tool for analysis of construction operations, as it allows the representation of actual, large-scale projects as manageable computer models. Considering the complexity of construction projects, simulation has been used for various construction applications from the conception phase to operation and maintenance (Lee et al. 2013). Considering the critical role of labor as a primary resource in the construction industry (Jarkas and Bitar 2011), effective modeling and scheduling of manual operations can be highly beneficial for improving productivity and safety of projects. Thus, simulation modeling can be adapted to represent existing and non-existing manual activities, obtain a reliable estimation of duration of manual operations, evaluate different scenarios of carrying out manual tasks, and assess the level of efficiency and safety of processes. Despite the effectiveness of simulation in modeling manual operations, in practice, its implementation and interpretation may be difficult to adapt for all team members and decision-makers, as the provided information may not be adequately detailed (Han et al. 2012). This issue can be resolved by using visualization in © ASCE 1 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. conjunction with simulation (Kamat and Martinez 2001) to provide information that is easy to understand for all project participants, to prevent misunderstandings of workplace and process designs, to facilitate implementation of operation designs, and to detect errors and collisions. In addition to the aforementioned advantages, using visualization of manual construction operations allows for the incorporation of human motion capture data into the workplace design process, which can be highly effective for ergonomic safety evaluation, realistic animation of manual activities, and worker training. Despite the various benefits of using motion capture data, due to issues such as lack of knowledge regarding the generation and use of motion data for analysis, potential cost of required equipment, software, and training of personnel, and lack of tools and methods for conveniently implementing motion analysis, less attention has been given to adapting it in construction practice. Thus, this study proposes a post-simulation visualization framework for manual construction operations, which uses motion capture data to provide a reliable and realistic representation of manual tasks. The proposed approach aims to enable simplified and effective simulation and visualization, which can be used by construction practitioners to improve the process of workplace design and facilitate efficiency and safety analysis of manual operations. RESEARCH FRAMEWORK The framework for visualizing manual operations using motion capture data is shown in Figure 1. A simulation model that represents the motions carried out to complete a manual task is first created. This modeling is enabled by incorporating Predetermined Motion Time Systems (PMTS) into a discrete event simulation environment (detailed descriptions can be found in Golabchi et al. (2016b) and Golabchi et al. (2015c)). The Special Purpose Simulation (SPS) template containing the modeling elements representing worker motions, developed in Simphony (Hajjar and AbouRizk 1999), can also include an ergonomic evaluation of the motions (Golabchi et al. 2015a). The modeling elements of a simulation model created using the SPS template represent different motions and tasks (e.g., walk, get object, carry object) and can be built using simple design data (e.g., walking distances, approximate shape and weight of object). © ASCE 2 Computing in Civil Engineering 2017 3 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Motion Database Simulation - Activity sequence - Task requirements - Operation methods - Labor resources Human Model Visualization - Workplace design - Worker motions - Process animation - Resource interaction BIM / CAD - Site layout - Equipment - Material - Tools Figure 1. Framework of simulation-based visualization of manual operations. After running the simulation model, a trace message is generated that contains information regarding the motions that are carried out, the sequence of actions, and the time of each event. This information is used as the input of an algorithm, which is connected to a motion capture database. Figure 2 shows the pseudo code of the algorithm. Based on the sequence of activities, the algorithm queries motions from the appropriate motion database and connects them together to create the full motion. A motion database exists for each type of motion, which is based on motion classes of the PMTS used in the simulation. For example, for the get motion, which represents the task of grasping an object, the database includes grasping motions with different types of grasps and different start and end locations. These motions are created previously using motion sensors (e.g., Microsoft Kinect), are stored in the corresponding database in the BVH format, and are appropriately tagged to enable querying. Using the input from the simulation model, the algorithm generates the final motion from the databases of basic motions. © ASCE Computing in Civil Engineering 2017 4 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Load motion databases Read input (trace message from simulation) For all motions in input If motion is GET New motion = old motion + GET Else if motion is PUT New motion = old motion + PUT … End if End for Save final motion Figure 2. Pseudo code of algorithm. After the final motion is created, it is saved as a BVH file. To bring this motion to the visualization environment, it must be added to a human mesh. The worker model, with the motion attached to it, is then imported into the final virtual model. This virtual model also includes 3D models of the workplace, including equipment, tools, and structures, and, for accurate representation, can potentially be extracted from the Building Information Model (BIM) (Golabchi et al. 2015b) or from point cloud models of the jobsite (Golabchi et al. 2016a). After importing the worker model, start and end positions of the motion must be set to match the position of the worker with the location of objects in the virtual model. Implementation of the process is detailed in the following section. IMPLEMENTATION The proposed framework is implemented on a simple manual task to demonstrate its functionality. The sequence of activities includes walking 3 meters, grasping an object from the floor, walking 4 meters, placing the object on a table, and walking 2 meters. As the first step, the simulation model is created in the Symphony modeling © ASCE Computing in Civil Engineering 2017 5 environment. The MODAPTS (Heyde 1966) standard is used as the PMTS system incorporated into the simulation, although any other PMTS may be similarly used. The model and the inputs required for the various modeling elements are shown in Figure 3. As shown in the figure, the required inputs include simple workplace attributes that are available when designing or redesigning a workplace. Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/04/19. Copyright ASCE. For personal use only; all rights reserved. Walk distance: 3 m Get condition: simple grasp Load: 4 kg Move distance: 18 in Move distance: 12 in Walk distance: 4 m Move distance: 18 in Move distance: 12 in Put condition: exact location Walk distance: 2 m Figure 3. Simulation model with required inputs. After running the simulation model, a trace message that includes the name and type of each motion, along with the time that it is carried out, is generated. This information is input into the algorithm (code developed in MATLAB) and the BVH motion file is compiled using the database of prerecorded BVH basic motions, as previously described. The final BVH motion then needs to be attached to the worker mesh, as shown in Figure 4. In this study, Autodesk MotionBuilder is used for this purpose. As shown in the figure, the biped character and the BVH motion are separate when imported, and a mapping of body joints must be completed for synchronization. This is achieved by adding a character to the scene, selecting different body joints on the skeleton from the BVH motion, and adding them to the corresponding mapping list of the character in the character definition window. This character can then be used as the source of the worker mesh. At this point, the mesh and the motion are connected and can be exported in the FBX format to insert into the virtual model. It is advised that the character and the first frame of the BVH file be in the T-pose to achieve correct mapping. © 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 Figure 4.. Linking BV VH motion to worker m mesh. Thee 3D model of the workp place and th he worker moodel and mootions must bbe imported into o a 3D modeeling enviro onment platfo form for inteegration. Thee Autodesk 3ds Max is useed in this stu udy, as it enaables effectiv ve importingg of BIM andd CAD moddels, as well as worker w mod del and motions. The BIIM model iss first importted into 3dss Max as an FBX X file, and all necessary y 3D modells that are nnot availablee in the BIM M model are sub bsequently ad dded. The worker w motion is also impported as an FBX file. A At this stage, the start and en nd point of motions m musst be appointted in the 3D D model to aanimate the worrker motion in the correect location of the 3D m model. This iis achieved bby defining anim mation keyss in the appro opriate fram mes of the mootion animattion in the trrack bar. At thiss stage, the virtual mo odel is com mplete and rready to usse. Figure 5 illustrates snaapshots of a virtual mod del in 3ds Max, where a BIM model is first impported from Auttodesk Reviit, 3D modeels of equipm ment and m material are added, and the worker model and motion are also imported. Figure 5. 5 Virtual model m with w worker motiions. © ASCE 6