Login | Register

Material Management Framework utilizing Near Real-Time Monitoring of Construction Operations


Material Management Framework utilizing Near Real-Time Monitoring of Construction Operations

Golkhoo, Farzaneh (2020) Material Management Framework utilizing Near Real-Time Monitoring of Construction Operations. PhD thesis, Concordia University.

[thumbnail of Golkhoo_PhD_F2020.pdf]
Text (application/pdf)
Golkhoo_PhD_F2020.pdf - Accepted Version
Available under License Spectrum Terms of Access.


Materials management is a vital process in the delivery of construction facilities. Studies by the Construction Industry Institute (CII) have demonstrated that materials and installed equipment can constitute 40– 70% of the total construction hard cost and affect 80% of the project schedule. Despite its significance, most of the construction industry sectors are suffering from poor material management processes including inaccurate warehouse records, over-ordering and large surpluses of material at project completion, poor site storage practices, running out of materials, late deliveries, double-handling of components, out-of-specification material, and out of sequence deliveries which all result in low productivity, delay in construction and cost overruns. Inefficient material management can be attributed to the complex, unstructured, and dynamic nature of the construction industry, which has not been considered in a large number of studies available in this field.
The literature reveals that available computer-based materials management systems focus on (1) integration of the materials management functions, and (2) application of Automated Data Collection (ADC) technologies to collect materials localization and tracking data for their computerized materials management systems. Moreover in studies that focused on applying ADC technologies in construction materials management, positioning and tracking critical resources in construction sites, and identifying unique materials received at the job site are the main applications of their used technologies. Even though, various studies have improved materials management processes copiously in the construction industry, the benefits of considering the dynamic nature of construction (in terms of near real-time progress monitoring using state of the art technologies and techniques) and its integration with a dynamic materials management system have been left out. So, in contrast with other studies, this research presents a construction materials management framework capable of considering the dynamic nature of construction projects. It includes a vital component to monitor project progress in near real-time to estimate the installation and consumption of materials. This framework consists of three models: “preconstruction model,” “construction model,” and “data analysis and reporting model.” This framework enables (1) generation of optimized material delivery schedules based on Material Requirement Planning (MRP) and minimum total cost, (2) issuance of material Purchase Orders (POs) according to optimized delivery schedules, (3) tracking the status of POs (Expediting methods), (4) collection and assessment of material data as it arrives on site, (5) considering the inherent dynamics of construction operations by monitoring project progress to update project schedule and estimate near real-time consumption of materials and eventually (6) updating MRP and optimized delivery schedule frequently throughout the construction phase.
An optimized material delivery schedule and an optimized purchase schedule with the least cost are generated by the preconstruction model to avoid consequences of early/late purchasing and excess/inadequate purchasing. Accurate assessment of project progress and estimation of installed or consumed materials are essential for an effective construction material management system. The construction model focuses on the collection of near real-time site data using ADC technologies. Project progress is visualized from two different perspectives, comparing as-built with as-planned and comparing various as-built status captured on consecutive points of time. Due to the recent improvements in digital photography and webcams, which made this technology more cost-effective and practical for monitoring project progress, digital imaging (including 360° images) is selected and applied for project progress monitoring in the construction (data acquisition) model. In the last model, which is the data analysis and reporting model, Deep Learning (DL) and image processing algorithms are proposed to visualize and detect actual progress in terms of built elements in near real-time. In contrast with the other studies in which conventional computer vision algorithms are often used to monitor projects progress, in this research, a deep Convolutional Auto-Encoder (CAE) and Mask Region-based Convolutional Neural Network (R-CNN) are utilized to facilitate vision-based indoor and outdoor progress monitoring of construction operations. The updated project schedule based on the actual progress is the output of this model, and it is used as the primary input for the developed material management framework to update MRP, optimized material delivery, and purchase schedules, respectively. Applicability of the models in the developed material management framework has been tested through laboratory and field experiments. The results demonstrated the accuracy and capabilities of the developed models in the framework.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Golkhoo, Farzaneh
Institution:Concordia University
Degree Name:Ph. D.
Program:Building Engineering
Date:20 August 2020
Thesis Supervisor(s):Moselhi, Osama
Keywords:Construction Materials Management, Progress Monitoring, Deep Learning, Optimization, Object Detection
ID Code:987386
Deposited On:27 Oct 2022 13:51
Last Modified:28 Oct 2022 00:00


Abdulla, W. (2017). “Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow.” Accessed: June 15, 2019. https://github.com/matterport/Mask_RCNN
Abeid, J., Allouche, E., Arditi, D., and Hayman, M. (2003). "PHOTO-NET II: a computer-based monitoring system applied to project management." Automation in Construction, 12(5), 603-616.
Ahmadian, F.F.A., Akbarnezhad, A., Rashidi, T.H., and Waller, S.T. (2016). “Accounting for transport times in planning off-site shipment of construction materials.” Journal of Construction Engineering and Management, 142(1).
Ahmed, S., Azher S., Castillo, M., and Kappagantula, P. (2002). “Construction Delays in Florida: An Empirical Study.” Final Report, Florida International.
Ajayi, S.O., Oyedele, L.O., Akinade, O.O., Bilal, M., Alaka, H.A., and Owolabi, H.A. (2017). “Optimizing material procurement for construction waste minimization: an exploration of success factors.” Sustainable Materials and Technologies, 11, 38–46.
Alavi, A. H., and Gandomi, A. H. (2017). “Big data in civil engineering.” Automation in Construction, 79, 1-2.
Allahkarami E., Nuri O.S., Abdollahzadeh A., Rezai B., and Maghsoudi B. (2017). “Improving estimation accuracy of metallurgical performance of industrial flotation process by using hybrid genetic algorithm – artificial neural network (GA-ANN).” Physicochemical Problems of Mineral Processing, 53(1), 366−378.
Al-Momani, A.H. (2000). “Construction Delay: A Quantitative Analysis.” International Journal of Project Management, 18, 51-59.
Alom, Md. Z. (2018). “Improved Deep Convolutional Neural Networks (DCNN) Approaches for Computer Vision and Bio-Medical Imaging.” Ph.D. thesis, University of Dayton, Dayton, Ohio.
Alqahtani A., Xie, X., Deng, J., and Jones, M. W. (2018). “A deep convolutional auto-encoder with embedded clustering.” In 25th IEEE International Conference on Image Processing (ICIP), 4058–4062.
Alshibani, A., and Moselhi, O. (2007). “Tracking and forecasting performance of earthmoving operations using GPS data.” CME 25 Conference Construction Management and Economics, 1377-1388.
Artescan, 3d Laser Scanning History, written in May 2012, http://artescan.net/blog/3-d-laser-scanner-history/, Last seen December 2016.
ASTM. ASTM E2544 09B Standard terminology for three-dimensional (3D) imaging systems. Technical report, 2009.
Atlas RFID Store, (2016). A trusted source in the RFID hardware industry, www.atlasrfidstore.com.
Azarm, R. (2013). “Material Status Index for Tracking and Progress Reporting of Construction Projects.” MSc. Theses, Concordia University, Montreal, Canada.
Bae, V, Golparvar-Fard, M, & White, J. (2013). “High-precision vision-based mobile augmented reality system for context-aware architectural, engineering, construction and facility management (AEC/FM) applications.” Visualization in Engineering, 1–3.
Bailey, P., and Farmer, D. (1982). “Materials Management Handbook.” Gower Publishing Company Limited, Aldershot, Hants, England.
Bansal, V. K., and Pal, M. (2009). "Extended GIS for construction engineering by adding direct sunlight visualizations on buildings." Construction Innovation, 9(4), 406 – 419.
Barry, W., Leite, F., and O'Brien, W. (2014). “Identification of Late Deliverables and Their True Effects on Industrial Construction Projects.” Construction Research Congress, 2296-2305.
Behzadan, A.H., Aziz, Z., Anumba, C.J., and Kamat, V.R. (2008). “Ubiquitous location tracking for context-specific information delivery on construction sites.” Journal of Automation in Construction, 17, 737-748.
Behzadan, A.H., and Kamat, V.R. (2013). “Enabling discovery-based learning in construction using telepresent augmented reality.” Automation in Construction, 33, 3–10.
Behzadan, A.H., Dong, S., Kamat, V.R. (2015). “Augmented reality visualization: A review of civil infrastructure system applications.” Advanced Engineering Informatics, 29 (2), 252–267.
Bell, L. C., and Stukhart, G. (1986). "Attributes of Materials Management Systems." Journal of Construction Engineering and Management, 112(1), 14-21.
Bell, L., and Stukhart, G. (1987). "Costs and Benefits of Materials Management Systems." Journal of Construction Engineering and Management, 113(2), 222-234.
Bell, L.C., and McCullouch, B.G. (1988). “Barcode application in construction.” Journal of Construction Engineering and Management, 114 (2), 263–278.
Bennett, C. L., and Ross, G. F. (1978). “Time-domain electromagnetics and its applications.” Proceeding IEEE, 66, 299–318.
Bernold, L.E. (1990a). “Barcode-driven equipment and materials tracking for construction.” Journal of Computing in Civil Engineering, 4(4), 381-395.
Bernold, L.E. (1990b). “Testing bar-code technology in construction environment.” Journal of Construction Engineering and Management, 116 (4), 643–655.
Bhatla, A., Choe, S. Y., Fierro, O., and Leite, F. (2012). “Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras.” Automation in Construction 28, 116–127.
Bognot, J. R., Candido, C. G., Blanco, A. C., and Montelibano, J. R. Y. (2018). "Building Construction Progress Monitoring Using Unmanned Aerial System (UAS), Low-Cost Photogrammetry, and Geographic Information System (GIS)." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 41-47.
Bosché, F., and Haas, C. T. (2008). "Automated retrieval of 3D CAD model objects in construction range images." Automation in Construction, 17(4), 499-512.
Bosché, F., Haas, C. T., and Akinci, B. (2009). "Automated Recognition of 3D CAD Objects in Site Laser Scans for Project 3D Status Visualization and Performance Control." Journal of Computing in Civil Engineering, 23(6), 311-318.
Bosché, F. (2010). “Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction.” Advanced Engineering Informatics, 24(1), 107–118.
Bosché, F., Guillemet A., Turkan, Y., Haas, C. T., and Haas, R. (2014). "Tracking the Built Status of MEP Works: Assessing the Value of a Scan-vs-BIM System." Journal of Computing in Civil Engineering, 28(4).
Braun, A., Borrmann, A. (2019). “Combining inverse photogrammetry and BIM for automated labeling of construction site images for machine learning.” Automation in Construction, 106.
Breed, G. (2005). “A Summary of FCC Rules for Ultra Wideband Communications.” High Frequency Electronics, 42–44.
Brilakis, I. (2007). “Long distance wireless networking for site - office data communications.” Journal of Information Technology in Construction (ITCON), 12, 154-164.
Brilakis, I., and Soibelman, L. (2008). “Shape-based retrieval of construction site photographs.” Journal of Computing in Civil Engineering, 22, 14–20.
Butcher, J. B., Day, C. R., Austin, J. C., Haycock, P. W., Verstraeten, D., and Schrauwen, B. (2014). “Defect detection in reinforced concrete using random neural architectures.” Computing Aided Civil Infrastructure Engineering, 29, 191–207.
Caladas, C. H, Torrent, D. G., and Haas, C. T. (2006). “Using Global Positioning System to Improve Materials-Locating Processes on Industrial Projects.” Journal of Construction Engineering and Management, 132(7), 741-749.
Caldas, C. H, Menches, C., Reyes, P., Navarro, L., and Vargas, D. (2015). "Materials Management Practices in the Construction Industry." Practice Periodical on Structural Design and Construction, 20(3).
Carr, J. (2014). “An introduction to genetic algorithms.” Senior Project, 1 – 40.
Cha Y. J., and Choi W. (2017). “Vision-Based Concrete Crack Detection using a Convolutional Neural Network.” In: Caicedo J., Pakzad S. (eds) Dynamics of Civil Structures, Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham, 2, 71–3.
Chandler, Ian E. (1978). “Materials Management on Building Sites.” The Construction Press Ltd, Lancaster, England.
Chapman. I, Olomolaiye, P., and Harris, F. (1990). “Automation problems in materials management on large construction projects.” Proceedings of the 7th ISARC, Bristol, United Kingdom, 499-504.
Chase, W. G., and Simon, H. A. (1973). "Perception in chess." Cognitive Psychology, 4(1), 55-81.
Chen, Z., Li, H., and Wong, C. T. C. (2002). "An application of bar-code system for reducing construction wastes." Automation in Construction, 11(5), 521-533.
Cheng, M-Y., and Chen, J-C. (2002). “Integrating barcode and GIS for monitoring construction progress.” Automation in Construction, 11 (2002), 23-33.
Cheng, T., Venugopal, M., Teizer, J., and Vela, P.A. (2011). “Performance evaluation of Ultra Wide Band technology for construction resource location tracking in harsh environments.” Automation in Construction, 20(8), 1173–1184.
Cheng, T., and Teizer, J. (2013). “Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications.” Automation in Construction, 34, 3–15.
Chi, S., and Caldas, C. (2011). “Automated object identification using optical video cameras on construction sites.” Computer-Aided Civil and Infrastructure Engineering, 26(5), 398-380.
Chin, S., and Yoon, S. (2008). “RFID+4D CAD for Progress Management of Structural Steel Works in High-Rise Buildings.” Journal of Computing in Civil Engineering, 22(2), 74-89.
Clevert, D., Unterthiner, T., and Hochreiter, S. (2015). "Fast and accurate deep network learning by exponential linear units (ELUs)." 5th International Conference on Learning Representations.
Construction Industry Institute (CII). (1999). “Procurement and Material Management: A Guide to Effective Project Execution.” The University of Texas at Austin.
Construction Industry Institute (CII). (2011). “Global Procurement and Material Management: An eGuide to Effective Project Execution.” The University of Texas at Austin.
Dakhli, Z., & Lafhaj, Z. (2018). “Considering materials management in construction: An exploratory study.” Logistics, 2(1), 1-13.
Davidson, I., and Skibniewski, M. (1995). "Simulation of Automated Data Collection in Buildings." Journal of Computing in Civil Engineering, 9(1), 9-20.
Deng, Y., Gan, V. J. L., Das, M., Cheng, J. C. P, and Anumba, C. (2019). “Integrating 4D BIM and GIS for Construction Supply Chain Management.” Journal of Construction Engineering and Management, 145(4).
Department of Defense of USA, 2008, “Global Positioning System Standard Positioning Service Signal Specification.” 4th Edition.
Ding, L., Fang, W., Luo, H., Love, P. E. D., Zhong, B., and Ouyang, X. (2018). “A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory.” Automation in Construction, 86, 118–124.
Divya, M., Janet, J., and Suguna, R. (2014). “A genetic optimized neural network for image retrieval in telemedicine.” EURASIP Journal on Image and Video Processing, 1–9.
Dimitrov, A., and Golparvar-Fard, M. (2014). "Vision-based material recognition for automated monitoring of construction progress and generating building information modeling from unordered site image collections." Advanced Engineering Informatics, 28(1), 37-49.
Dong, S., Kamat, V.R. (2013). “SMART: scalable and modular augmented reality template for rapid development of engineering visualization applications.” Journal of Visualization in Engineering, 1(1).
Dutta, A., and Zisserman, A. (2019). “The VIA Annotation Software for Images, Audio and Video.” In Proceedings of the 27th ACM International Conference on Multimedia (MM ’19), Nice, France. Accessed: June 5, 2019. http://www.robots.ox.ac.uk/~vgg/software/via/
Eirini, K., and Ioannis, B. (2018). “Matching construction workers across views for automated 3d vision tracking on-site.” Journal of Construction Engineering and Management, 144 (7).
El-Omari, S. (2008), “Automated data acquisition for tracking and control of construction projects.” Ph.D. thesis, Concordia University, Montreal, Canada.
El-Qader Al Haddad, E. A. (2006). “A construction materials management system for Gaza strip building contractors.” MSc. thesis, The Islamic University of Gaza.
Elzarka, H., and Bell, L. (1995). "Object-Oriented Methodology for Materials-Management Systems." Journal of Construction Engineering and Management, 121(4), 438-445.
Ergen, E., Akini, B., and Sacks S. (2007). “Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS.” Journal of Automation in Construction, 16, 354-367.
Eiris Pereira R., Moud H.I., and Gheisari M. (2017). “Using 360-Degree Interactive Panoramas to Develop Virtual Representation of Construction Sites.” In: Proceeding of Lean & Computing in Construction Congress (LC3), Vol. 3 (CONVR), Heraklion, Greece, 775-782.
Eiris Pereira, R., and Gheisari, M. (2018). “360-Degree Panoramas as a Reality Capturing Technique in Construction Domain: Applications and Limitations.” 55th ASC Annual International Conference Proceedings, Denver, Colorado.
Fallahnejad, M. H. (2013). “Delay causes in Iran gas pipeline projects.” International Journal of Project Management, 31(1), 136–146.
Fang, Y., and Ng, S. T. (2011). “Applying activity-based costing approach for construction logistics cost analysis.” Construction Innovation, 11 (3), 259–281.
Fathi, H., Dai, F., and Lourakis, M. (2015). “Automated as-built 3D reconstruction of civil infrastructure using computer vision: Achievements, opportunities, and challenges.” Advanced Engineering Informatics, 29(2), 149–161.
Formoso, C.T., and Revelob, V.H. (1999). “Improving the materials supply system in small-sized building firms.” Automation in Construction, 8(6), 663–670.
Freimuth, H., and König, M. (2015). "Generation of waypoints for UAV-assisted progress monitoring and acceptance of construction work." In15th International Conference on Construction Applications of Virtual Reality (CONVR), Alberta, Canda.
Furukawa, Y., and Ponce, J. (2006). “High-fidelity image based modeling.” Technical Rep. 2006-02, Univ. of Illinois, Urbana IL.
Georgy, M., and Basily, S.Y. (2008). “Using genetic algorithms in optimizing construction material delivery schedules.” Construction Innovation, 8(1), 23-45.
Géron, A. (2017). “Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.” Sebastopol, CA: O'Reilly Media.
Gheisari, M., Sehat, N., and Williams, G. (2015). “Using Augmented Panoramic Views as an Online Course Delivery Mechanism in MOOCs.” 51st ASC Annual International Conference Proceedings, Washington DC.
Gheisari, M., Sabzevar, M. F., Chen, P., and Irizarry, J. (2016). “Integrating BIM and Panorama to Create a Semi-Augmented-Reality Experience of a Construction Site.” International Journal of Construction Education and Research, 12(4).
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). “Rich feature hierarchies for accurate object detection and semantic segmentation.” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 580–587.
Girshick, R. (2015). “Fast R-CNN.” 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 1440–1448.
Glorot, X., and Bengio, Y. (2010). "Understanding the difficulty of training deep feedforward neural networks." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 9, 249–256.
Glorot, X., Bordes, A., and Bengio, Y. (2011). "Deep sparse rectifier neural networks." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2011), 15, 315–323.
Golkhoo, F., and Moselhi, O. (2019). “Optimized material management in construction using multi-layer perceptron.” Canadian Journal of Civil Engineering, 46(10), 909-923.
Golparvar-Fard, M. (2006). "Assessment of Collaborative Decision-Making in Design Development and Coordination Meetings." The University of British Columbia.
Golparvar-Fard, M., and Peña-Mora, F. (2007). “Application of visualization techniques for construction progress monitoring.” In Proceedings of the ASCE International Workshop on Computing in Civil Engineering, Pittsburgh, PA, 261(27), 216-223.
Golparvar-Fard, M., Peña-Mora, F., Arboleda, C. A., and Lee, S. (2009). "Visualization of Construction Progress Monitoring with 4D Simulation Model Overlaid on Time-Lapsed Photographs." Journal of Computing in Civil Engineering, 23(6), 391-404.
Golparvar-Fard, M., Peña-Mora, F., and Savarese, S. (2011). "Integrated Sequential As-Built and As-Planned Representation with D4AR Tools in Support of Decision-Making Tasks in the AEC/FM Industry." Journal of Construction Engineering and Management, 137(12), 1099-1116.
Golparvar-Fard, M., Peña-Mora, F., and Savarese, S. (2015). "Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models." Journal of Computing in Civil Engineering, 29(1), 147-165.
Gong, J., and Caldas, C. (2007). “Processing of High Frequency Local Area Laser Scans for Construction Site Resource Management.” Computing in Civil Engineering, 665-672.
Gonzalez, R. C., Woods, R. E., and Eddins, S. L. (2009). “Digital Image processing using MATLAB®.” United States: Gatesmark Publishing.
Goodrum, P.M., McLaren, M.A., and Durfee, A. (2006). “The application of active radio frequency identification technology for tool tracking on construction job sites.” Journal of Automation in Construction, 15(3), 292-302.
Gordon, C., Boukamp, F., Huber, D., Latimer, E., Park, K., and Akinci, B. (2003). “Combining reality capture technologies for construction defect detection: A case study.” Proc., 9th EuropIA Int. Conf. (EIA9), Istanbul, Turkey, 99–108.
Gurmu, A. T. (2018). “Construction materials management practices enhancing labour productivity in multi-story building projects.” International Journal of Construction Management, 1-10.
Gurmu, A. T. (2019). “Tools for measuring construction materials management practices and predicting labor productivity in multistory building projects.” Journal of construction engineering and management, 145(2), 1-13.
Guo, W. (2008). “Automated Defect Detection and Recognition for Wastewater Infrastructure Inspection and Condition Assessment.” Ph.D. thesis, Carnegie Mellon University, Pittsburgh, US.
Halpin D.W., Escalona A.L., and Szmurlo P.M. (1987). “Work Packaging for Project Control.” Construction Industry Institute, Austin, TX.
Hamledari, H., McCabe, B., and Davari, S. (2017). "Automated computer vision-based detection of components of under-construction indoor partitions." Automation in Construction, 74, 78-94.
Han, S., Lee, S., and Peña-Mora, F. (2012). “Vision-Based Motion Detection for Safety Behavior Analysis in Construction.” Construction Research Congress, 2012. 1032-1041. The USA.
Han, K. K., and Golparvar-Fard, M. (2017). “Potential of big visual data and building information modeling for construction performance analytics: An exploratory study.” Automation in Construction, 73, 184–198.
Hattori, H., Naresh Boddeti, V., Kitani, K.M., Kanade, T. (2015). “Learning scene-specific pedestrian detectors without real data.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA.
He, K., Gkioxari G., Doll´ar P., Girshick R. (2017). “Mask RCNN.” The IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2961-2969.
Hedgepeth, W.O. (2007). “RFID METRICS Decision Making Tools for Today’s Supply Chains.” CRC Press, Taylor & Francis Group, Boca Raton, FL.
Hinterstoisser, S., Lepetit, V., Wohlhart, P., and Konolige, K. (2017). "On pre-trained image features and synthetic images for deep learning." in European Conference on Computer Vision (ECCV) Workshops, Munich, Germany, 682-697.
Holland, J.H. (1975). “Adaptation in Natural and Artificial Systems.” Ann Arbor: University of Michigan Press, MIT Press, Cambridge 183 pp.
HoloBuilder, 2019, “The Ultimate 2019 Construction 360° Camera Guide.” Available: https://holobuilder.link/camera_guide
Hou, X., Zeng, Y., and Xue, J. (2020). “Detecting Structural Components of Building Engineering Based on Deep-Learning Method.” Journal of Construction Engineering and Management, 146(2).
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., and Guadarrama S. (2017). “Speed/accuracy trade-offs for modern convolutional object detectors.” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 7310-7311.
Huertas, A., and Nevatia, R. (2000). “Detecting changes in aerial views of man-made structures.” Image and Vision Computing, 18(8), 583–596.
Ibrahim, Y. M., Lukins, T.C., Zhang, X., Trucco, E., and Kaka, A.P. (2009). "Towards automated progress assessment of work package components in construction projects using computer vision." Advanced Engineering Informatics, 23(1), 93-103.
Irizarry, J., Karan, E. P., and Jalaei, F. (2013). “Integrating BIM and GIS to improve the visual monitoring of construction supply chain management.” Automation in Construction, 31, 241-254.
Iyer, K. C., and Jha, K. N. (2005). "Factors affecting cost performance: evidence from Indian construction projects." International Journal of Project Management, 23(4), 283-295.
Jalal, M., Spjut, J., Boudaoud, B., and Betke, M. (2019). “Sidod: A synthetic image dataset for 3d object pose recognition with distractors.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.
Jalali, S. (2007). “Quantification of construction waste amount.” 6th International technical conference of waste, Viseu, Portugal.
Jang, W. S. (2007). “Embedded system for construction material tracking Using combination of radiofrequency and Ultrasound signal.” Ph.D. thesis, University of Maryland, US.
Jang, H., Lee, S., and Choi, S. (2007). “Optimization of floor-level construction material layout using Genetic Algorithms.” Automation in Construction, 16(4), 531–545.
Jaselskis, E., Anderson, M., Jahren, C., Rodriguez, Y., and Njos, S. (1995). "Radio-Frequency Identification Applications in Construction Industry." Journal of Construction Engineering and Management, 121(2), 189-196.
Jaselskis, E., and El-Misalami, T. (2003). "Implementing Radio Frequency Identification in the Construction Process." Journal of Construction Engineering and Management, 129(6), 680-688.
Jaśkowski, P., Sobotka, A., Czarnigowska, A. (2018). “Decision model for planning material supply channels in construction.” Automation in Construction, 90, 235-242.
Jung, M., Park, M., Lee, H.S., and Chi, S. (2018). “Multimethod Supply Chain Simulation Model for High-Rise Building Construction Projects.” Journal of Computing in Civil Engineering, 32(3).
Kasim, N. (2008). “Improving materials management on construction projects.” Ph.D. thesis, Loughborough University, United Kingdom.
Kasim, N. (2015). “Intelligent Materials Tracking System for Construction Projects Management.” Journal of Engineering and Technological Sciences, 47(2), 218-230.
Kasim, N., Sarpin, N., Mohd Noh, H., Zainal, R., Mohamed, S., Manap, N., and Yahya, M., Y. (2019). “Automatic materials tracking practices through RFID implementation in construction projects.” In: MATEC Web of Conferences (EDP Sciences), 1-5.
Kerridge, A.E. (1987). “Manage material effectively (part I).” Hydrocarbon Processing, 63-71.
Khoury, H.M., and Kamat, V.R. (2009). “Evaluation of position tracking technologies for user localization in indoor construction environments.” Journal of Automation in Construction, 18, 444-457.
Kim, H., and Kano, N. (2008). "Comparison of construction photograph and VR image in construction progress." Automation in Construction, 17(2), 137-143.
Kim, H., Kim, H., Hong, Y. W. and Byun, H. (2018). “Detecting Construction Equipment Using a Region-Based Fully Convolutional Network and Transfer Learning.” Journal of Computing in Civil Engineering, 32(2).
Kim, C., Son, H., and Kim, C. (2013a). "Automated construction progress measurement using a 4D building information model and 3D data." Automation in Construction, 31, 75-82.
Kim, C., Kim, B., and Kim, H. (2013b). "4D CAD model updating using image processing-based construction progress monitoring." Automation in Construction, 35, 44-52.
Kini, D. (1999). "Materials Management: The Key to Successful Project Management." Journal of Management in Engineering, 15(1), 30-34.
Kirk, K. E. (2010). “Genetic Algorithms and an Exploration of the Genetic Wavelet Algorithm.” Master thesis, Villanova University, Pennsylvania.
Kivrak, S., and Arslan, G. (2019). “Using Augmented Reality to Facilitate Construction Site Activities.” Advances in Informatics and Computing in Civil and Construction Engineering, 215-221.
Kiziltas, S., Akinci, B., Ergen, E., and Tang, P. (2008). “Technological assessment and process implications of field data capture technologies for construction and facility/infrastructure management.” Journal of Information Technology in Construction, 13, 134–154.
Klein, L., Li, N., and Becerik-Gerber, B. (2012). "Imaged-based verification of as-built documentation of operational buildings." Automation in Construction, 21, 161-171.
Koch, C., Paal, S., Rashidi, A., Zhu, Z., König, M., and Brilakis, L. (2014). “Achievements and challenges in machine vision-based inspection of large concrete structures.” Advances in Structural Engineering, 17 (3), 303–318.
Kong, C.W., Li, H. r., Love, P.E.D. (2001). “An E-commerce System for Construction Material Procurement.” Construction Innovation, 1(1), 43-54.
Kong X., and Li J. (2018). “Vision‐based fatigue crack detection of steel structures using video feature tracking.” Computing in Civil Infrastructure Engineering, 33(9), 783-799.
Kopsida, M., Brilakis, I., and Vela, P. A. (2015). "A review of automated construction progress monitoring and inspection methods." in Proceedings of the 32nd CIB W78 Conference, Eindhoven, Netherlands.
Kopsida, M., and Brilakis, I. (2020). “Real-Time Volume-to-Plane Comparison for Mixed Reality–Based Progress Monitoring.” Journal of Computing in Civil Engineering, 2020, 34(4).
Kropp, C., Koch, C., and König, M. (2018). "Interior construction state recognition with 4D BIM registered image sequences." Automation in Construction, 86, 11-32.
Kumar, A. (2010). “Lean Construction in the Building Industry.” Technical Report, University of Illinois, Urbana-Champaign.
Le, K. T. (2017). “Material Inventory Control and Management System in Construction Using GIS Applications and a "Hybrid" Tracking System.” Ph.D. thesis, Illinois Institute of Technology, Chicago, Illinois.
Lee, W. J., Song, J. H., Kwon, S. W., Chin, S., Choi, C., and Kim, Y. S. (2008). “A gate sensor for construction logistics.” Proceeding of 25th International Symposium on Automation and Robotics in Construction, Institute of Internet and Intelligent Technologies, Vilnius, Lithuania, 100–105.
Lee, A. (2016). “Comparing Deep Neural Networks and Traditional Vision Algorithms in Mobile Robotics.” Available: https://pdfs.semanticscholar.org/ 1b6f/569b79721037425fca034c7ae47904fb9276.pdf
Lei, L., Zhou, Y., Luo, H., and Love, P. E. D. (2019). “A CNN-based 3D patch registration approach for integrating sequential models in support of progress monitoring.” Advanced Engineering Informatics, 41.
Leng, B., Guo, S., Zhang, X., Xiong Z. (2015). "3D object retrieval with stacked local convolutional autoencoder." Signal Processing, 112, 119-128.
Li, H., Chen, Z., Yong, L., and Kong, S.C.W. (2005). “Application of integrated GPS and GIS technology for reducing construction waste and improving construction efficiency.” Automation in Construction, 14(2005), 323-331.
Lin, K., and Fang, J. (2013). "Applications of computer vision on tile alignment inspection." Automation in Construction, 35, 562-567.
Lin, Y.C., Cheung, W.F., and Siao F.C. (2014). “Developing mobile 2D barcode/RFID-based maintenance management system.” Automation in Construction, 37, 110-121.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., and Berg, A. C. (2016). “SSD: Single shot multibox detector.” European Conference on Computer Vision (ECCV 2016), Amsterdam, Netherlands, 21-37.
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., and Pietikainen, M. (2019). “Deep learning for generic object detection: A survey.” International Journal of Computer Vision, 1-58.
Lu, M., Chen, W., Shen, X., Lam, H.C., and Liu, J. (2007). “Positioning and tracking construction vehicles in highly dense urban areas and building construction sites.” Automation in Construction, 16, 647-656.
Lu, L., Wang, F., and Liu, R. (2011). “A Tracking Management Information System for Railway Construction Materials.” 11th International Conference of Chinese Transportation Professionals (ICCTP), 3742-3748.
Lu, H., Wang, H., Xie, Y., and Wang, X. (2018). “Study on construction material allocation policies: A simulation optimization method.” Automation in Construction, 90, 201–212.
Lu Q., Lee S., and Chen L. (2018). “Image-driven fuzzy based system to construct as-is IFC BIM objects.” Automation in Construction, 92, 68-87.
Luo, X., Li, H., Cao, D., Dai, F., Seo, J., and Lee, S. (2018). “Recognizing diverse construction activities in site images via relevance networks of construction-related objects detected by convolutional neural networks.” Journal of Computing in Civil Engineering, 32 (3).
Lytle, A. M. (2011). “A framework for object recognition in construction using building information modeling and high frame rate 3D imaging.” Ph.D. thesis, Virginia Polytechnic Institute and State University.
Ma, Z., Liu, Z., and Zhang, D. (2013). “An Integrated Mobile Material Management System for Construction Sites.” AEI, 2013, 354-363.
Maalek, R., and Sadeghpour, F. (2012). "Reliability assessment of Ultra Wide Band for indoor tracking of static resources on construction sites." in CSCE conferences, Edmonton, Alberta.
Macoir, N., Bauwens, J., Jooris, B., Van Herbruggen, B., Rossey, J., Hoebeke, J., and De Poorter, E. (2019). “UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management.” Sensors, 2019, 19, 467.
MajrouhiSardroud, J. (2012). “Influence of RFID technology on automated management of construction materials and components.” Scientia Iranica, 19 (3), 381–392.
Mahmood Maad, M., Noori Sadeq, A. (2019). “Reducing Waste of Construction Materials in Civil Engineering Projects in Iraq.” ZANCO Journal of Pure and Applied Sciences (ZJPAS), 31(s3), 257-263.
Masci, J., Meier, U., Cireşan, D., and Schmidhuber, J. (2011). "Stacked convolutional auto-encoders for hierarchical feature extraction." in 21th International Conference on Artificial Neural Networks (ICANN), 52-59.
Marktscheffel, A.A. (2020). “How to create a new Revit 360 rendering project with the HoloBuilder plug-in (manual view placing).” Available: https://help.holobuilder.com/en/articles/3158292-how-to-create-a-new-revit-360-rendering-project-with-the-holobuilder-plug-in-manual-view-placing
Matthews, J., Love, P., Heinemann, S., Chandler, R., Rumsey, C., Olatunj, O. (2015). “Real time progress management: Re-engineering processes for cloud-based BIM in construction.” Automation in Construction, 58, 38-47.
McCulloch, W. S., and Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity.” Bulletin of Mathematical Biology, 5(4), 115–133.
Memarzadeh, M., Golparvar-Fard, M., and Niebles, J. C. (2013). “Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors.” Automation in Construction, 32, 24–37.
Mneymneh, B. E., Abbas, M., and Khoury, H. (2018). “Evaluation of computer vision techniques for automated hardhat detection in indoor construction safety applications.” Frontiers of Engineering Management, 5(2), 227-239.
Montaser, A. (2013). “Automated Site Data Acquisition for Effective Project Control.” Ph.D. thesis, Concordia University, Montreal, Quebec, Canada.
Morgan, D., Scofield. P., and Christopher L. (1991). “Neural Networks and Speech Processing.” In: Neural Networks and Speech Processing. The Springer International Series in Engineering and Computer Science (VLSI, Computer Architecture and Digital Signal Processing), 130. Springer, Boston, MA.
Moselhi, O., Bardareh, H., Zhu, Z. (2020). “Automated Data Acquisition in Construction with Remote Sensing Technologies.” Applied Science, 10(8).
Movshovitz-Attias, Y., Naresh Boddeti, V., Wei, Z., Sheikh, Y. (2014). “3D pose-by-detection of vehicles via discriminatively reduced ensembles of correlation filters.” In Proceedings of the British Machine Vision Conference (BMVC), Nottingham, UK.
Movshovitz-attias, Y., Kanade, T., and Sheikh, Y. (2016). "How Useful is Photo-Realistic Rendering for Visual Learning?" In Computer Vision– ECCV 2016 Workshops, Amsterdam, Netherlands, 202-217.
Nair, V., and Hinton, G. E. (2010). "Rectified linear units improve restricted Boltzmann machines." Proceedings of the 27th International Conference on Machine Learning (ICML10), 807–814.
Nasir, H. (2008). “A model for automated construction materials tracking.” Master thesis, University of Waterloo, Waterloo, Canada.
Nasseri, M., Asghari, K., and Abedini, M.J. (2008). “Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network.” Expert Systems with Applications, 35(3), 1415-1421.
Nath, N. D., and Behzadan, A. H. (2019). “Deep Learning Models for Content-Based Retrieval of Construction Visual Data.” ASCE International Conference on Computing in Civil Engineering, Atlanta, Georgia.
Navon, R., and Goldschmidt, E. (2003). "Can Labor Inputs be Measured and Controlled Automatically?" Journal Construction Engineering Management, 129(4), 437-445.
Navon, R., Goldschmidt, E., and Shpatnisky Y. (2004). “A concept proving prototype of automated earthmoving control.” Automation in Construction, 13, 225–239.
Navon, R., and Shpatnitsky, Y. (2005). "Field Experiments in Automated Monitoring of Road Construction." Journal of Construction Engineering and Management, 131(4), 487-493.
Navon, R., and Berkovich, O. (2006). “An automated model for materials management and control.” Construction Management and Economics, 24(6), 635-646.
Navon, R. (2007). "Research in automated measurement of project performance indicators." Automation in Construction, 16(2), 176-188.
Nialidjoubi, L and Yang, J. L. (2001). “An Intelligent Materials Routing System on Complex Construction Sites. Logistics Information Management.” 14(516), 337-343.
Oliveira Filho, J., Su, Y., Song, H., Liu, L., and Hashash, Y. (2005). “Field Tests of 3D Laser Scanning in Urban Excavation.” Computing in Civil Engineering, 1-10.
Olsen, M., Kuester, F., Chang, B., and Hutchinson, T. (2010). "Terrestrial Laser Scanning-Based Structural Damage Assessment." Journal of Computing in Civil Engineering, 24(3), 264-272.
Oloufa, A.A., Ikeda, M., Oda, H. (2003). “Situational awareness of construction equipment using GPS wireless and web technologies.” Automation in Construction, 12 (6), 737-748.
Omar, T., and Nehdi, M. L. (2016). “Data acquisition technologies for construction progress tracking.” Automation in Construction, 70, 143–155.
Paine, T. L. (2017). “Practical Considerations for Deep Learning.” Ph.D. thesis, the University of Illinois at Urbana-Champaign, Urbana, Illinois.
Panthula, G. A. (2018). “Object Detection Techniques Using Convolutional Neural Networks.” Master thesis, Florida State University, Tallahassee, Florida.
Papon, J., and Schoeler, M. (2015). "Semantic pose using deep networks trained on synthetic RGB-D." in IEEE International Conference on Computer Vision (ICCV), 774-782.
Park, M.W., and Brilakis, I. (2016). “Continuous localization of construction workers via integration of detection and tracking.” Automation in Construction, 72, 129–142.
Perdomo, J., and Thabet, W. (2002). “Material Management Practices for the Electrical Contractor.” Computing in Civil Engineering, 232-243.
Peyret, F., and Tasky, R. (2002). “Asphalt quality parameters traceability using electronic tags and GPS.” Proceedings of International Symposium on Automation and Robotics in Construction, 19th (ISARC), NIST, Gaithersburg, Maryland, 155-160.
Pham, H.C., Dao, N., Pedro, A., Le, Q.T., Hussain, R., Cho, S., and Park, C. (2018). “Virtual Field Trip for Mobile Construction Safety Education Using 360-Degree Panoramic Virtual reality.” International Journal of Engineering Education, 34(4), 1174- 1191.
Plemmons, J., and Bell, L. (1995). "Measuring Effectiveness of Materials Management Process." Journal of Management in Engineering, 11(6), 26-32.
Polat, G., Arditi, D., and Mungen, U. (2007). "Simulation-Based Decision Support System for Economical Supply Chain Management of Rebar." Journal of Construction Engineering and Management, 133(1), 29-39.
Poon, C. S., Yu, A. T. W., Wong, S. W., and Cheung, E. (2004). “Management of construction waste in public housing projects in Hong Kong.” Construction management and economics, 22 (7), 675-689.
Poon, C. S., Yu, A. T. W., and Ng, L. H. (2001). “On-site sorting of construction and demolition waste in Hong Kong.” Resources, conservation and recycling, 32 (2), 157-172.
Poulton, M. (2001). “Computational neural networks and neural networks for geophysical data processing.” Pergamon, Amsterdam, Netherlands.
Pour Rahimiana, F., Seyedzadeh, S., Oliver, S., Rodriguez, S., and Dawood, N. (2020). “On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning.” Automation in Construction, 110.
Pučko, Z., Šuman, N., and Rebolj, D. (2018). "Automated continuous construction progress monitoring using multiple workplace real-time 3D scans." Advanced Engineering Informatics, 38, 27-40.
Rad, M., Oberweger, M., and Lepetit, V. (2018). “Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, 4663-4672.
Rahman, I., Memom, A., and Karim, A. (2013). “Relationship between Factors of Construction Resources Affecting Project Cost.” Modern Applied Science, 7(1), 67-75.
Rajpura, P. S., Hegde, R. S., and Bojinov, H. (2017). “Object detection using deep CNNs trained on synthetic images." Ithaca, NY: Cornell University.
Rankohi, S., and Waugh, L. (2013). “Review and analysis of augmented reality literature for construction industry.” Journal of Visualization in Engineering, 1–9.
Rankohi, S., and Waugh, L. (2014). "Image-based modeling approaches for projects status comparison." in CSCE 2014 General Conference, Halifax, Nova Scotia.
Rebolj, D., Čuš Babič, N., Magdič, A., Podbreznik, P., Pšunder, M. (2008). "Automated construction activity monitoring system." Advanced Engineering Informatics, 22(4), 493-503.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). “You Only Look Once: Unified, Real-Time Object Detection.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, Nevada, 779–788.
Ren, Z., Anumba, C.J., and Tah, J. (2011). “RFID-facilitated construction materials management (RFID-CMM) – A case study of water-supply project.” Advanced Engineering Informatics, 25(2), 198–207.
Ren, S., He, K., Girshick, R., and Sun, J. (2017). “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” in IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149.
Ren, X., Zhu, Z., Germain, C., Belair, R., and Chen, Z. (2017). “Automated monitoring of the utilization rate of onsite construction equipment.” In Proceeding of Computing in Civil Engineering, Reston, VA, 74–81.
RFID Journal (2016). RFID frequently asked questions, General RFID Information http://www.rfidjournal.com
RomRoc. (2018). Mask R-CNN instance segmentation with custom dataset in Google Colab, GitHub repository, Accessed: June 15, 2019. https://github.com/RomRoc/maskrcnn_train_tensorflow_colab
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A. M. (2016). "The SYNTHIA Dataset: A large collection of synthetic images for semantic segmentation of urban scenes." in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3234-3243.
Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). “Learning internal representations by error propagation.” In Parallel Distributed Processing.0 1: Foundations. MIT Press, Cambridge, MA.
Sacks, R., Treckmann, M., and Rozenfeld, O. (2009). "Visualization of Work Flow to Support Lean Construction." Journal of Construction Engineering and Management, 135(12), 1307-1315.
Sacks, R., Navon, R., and Goldschmidt, E. (2003). "Building Project Model Support for Automated Labor Monitoring." Journal of Computing in Civil Engineering, 17(1), 19-27.
Said, H., and El-Rayes, K. (2011). "Optimizing Material Procurement and Storage on Construction Sites." Journal of Construction Engineering and Management, 421-431.
Said, H., and El-Rayes, K. (2013). “Optimal utilization of interior building spaces for material procurement and storage in congested construction sites.” Automation in Construction, 31, 292–306.
Said, H., and El-Rayes, K. (2014). “Automated Multi-objective Construction Logistics Optimization System.” Automation in Construction, 43, 110-122.
Saidi, K.S., Lytle, A.M., and Stone, W.C. (2003). “Report of the NIST Workshop on Data Exchange Standards at the Construction Job Site.” in proceedings, ISARC 2003, The Future Site, Eindhoven Technical University, Eindhoven, 617-622.
Sakurada, K. (2015). “Four-dimensional City Modeling using Vehicular Imagery.” Ph.D. thesis, Tohoku University, Sendai, Japan.
Salimans, T., and Kingma, D. P. (2016). "Weight normalization: A simple reparameterization to accelerate training of deep neural networks." In Advances in Neural Information Processing Systems, 901-909.
Seiffert, U. (2001). “Multiple Layer Perceptron Training Using Genetic Algorithms.” ESANN-proceedings - European Symposium on Artificial Neural Networks, Bruges (Belgium), April, 159-164.
Siddula, M., Dai, F., Ye, Y., and Fan, J. (2016). “Unsupervised feature learning for objects of interest detection in cluttered construction roof site images.” Procedia Engineering, 145, 428–435.
Simon, D. (2013). “Evolutionary Optimization Algorithms.” John Wiley & Sons, Inc., Hoboken, New Jersey.
Shehab-Eldeen, T. (2001). “An automated system for detection, classification and rehabilitation of defects in sewer pipes.” Ph.D. thesis, Concordia University, Quebec, Canada.
Shirvany, Y., Hayatia, M., and Moradian, R. (2009). “Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations.” Applied Soft Computing, 9(1), 20-29.
Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., and Zemel, R. S. (2017). "Learning to generate images with perceptual similarity metrics." in IEEE International Conference on Image Processing (ICIP).
Soleimanifar, M. (2011). “IntelliSensorNet: A Positioning Technique Integrating Wireless Sensor Networks and Artificial Neural Networks for Critical Construction Resource Tracking.” M.Sc. thesis, University of Alberta.
Soleimanifar, M., Shen, X., Lu, M., and Nikolaidis, I. (2014). “Applying received signal strength based methods for indoor positioning and tracking in construction applications.” Canadian Journal of Civil Engineering, 41(8): 703-716.
Son, H., and Kim, C. (2010). "3D structural component recognition and modeling method using color and 3D data for construction progress monitoring." Automation in Construction, 19(7), 844-854.
Song, J., Haas, C.T., and Caldas, C.H. (2006a). “Tracking the location of materials on construction job sites.” Journal of Construction Engineering and Management, 132 (9), 911-918.
Song, J., Ergen, E., Haas, C.T., Akinci, B., and Caldas, C. (2006b). “Automating the task of tracking the delivery and receipt of fabricated pipe spools in industrial projects.” Journal of Automation in Construction, 15(2), 166-177.
Soudarissanane, S.S. (2016). “The geometry of terrestrial laser scanning; identification of errors, modeling and mitigation of scanning geometry.” Ph.D. thesis, Faculty Civil Engineering and Geosciences, Delft University of Technology, Netherlands.
Specht, C., Dąbrowski, P., Dumalski, A., and Hejbudzka, K. (2016). “Modeling 3D Objects for Navigation Purposes Using Laser Scanning.” International Journal on Marine Navigation and Safety of Sea Transportation, 10(2), 301-306.
Stukhart, G. (1995). “Construction Materials Management.” Marcel Dekker Inc. New York.
Su, Y., and Liu, L. (2007). “Real-Time Construction Operation Tracking from Resource Positions.” Computing in Civil Engineering, 200-207.
Su, H., Qi, C. R., Li, Y., and Guibas, L. (2015). "Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views." in IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2686-2694.
Subsomboon, K., Christodoulou, S., and Griffis, F. (2003). “Procurement of Services and Materials Using a FIAPP-Based System—New York City Case Studies.” Construction Research Congress, 1-9.
Sun, B., Saenko, K. (2014). “From virtual to reality: Fast adaptation of virtual object detectors to real domains.” In Proceedings of the British Machine Vision Conference (BMVC). Nottingham, United Kingdom.
Tahmasebi, P., and Hezarkhani, A. (2012). “A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation.” Computers &Geo sciences 42, 18–27.
Tang, P., Huber, D., Akinci, B., Lipman, R., and Lytle, A. (2010). "Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques." Automation in Construction, 19(7), 829-843.
Teizer, J., Lao, D., and Sofer, M. (2007). “Rapid Automated Monitoring Of Construction Site Activities Using Ultra-Wideband.” 24th International Symposium on Automation & Robotics in Construction (ISARC 2007).
Teizer, J., Venugopal M., and Walia, A. (2008). “Ultrawideband for Automated Real-Time Three-Dimensional Location Sensing for Workforce, Equipment, and Material Positioning and Tracking.” Transportation Research Record: Journal of the Transportation Research Board, 2081.
The Business Roundtable. (1982). "Modern Management Systems." Construction Industry Cost Effectiveness Report A-6, New York.
Thomas, H., Sanvido, V., and Sanders, S. (1989). "Impact of Material Management on Productivity—a Case Study." Journal of Construction Engineering and Management, 115(3), 370-384.
Thomas, H. R., Riley, D. R., and Sanvido, V. E. (1999). "Loss of Labor Productivity due to Delivery Methods and Weather." Journal of Construction Engineering and Management, 125(1), 39-46.
Thomas, H. R., and Sandivo, V. E. (2000). “Role of the fabricator in labor productivity.” Journal of Construction Engineering and Management, 126 (5), 358-365.
Thomas, H.R., Riley, D.R., and Messner, J.I. (2005). “Fundamental principles of site material management.” Journal of Construction Engineering and Management, ASCE, 131(7), 808–15.
Tian, G., Zhang, X., and Hu, F. (2012). “The Design of Electric Materials Management System Based on QR-Code.” ICLEM, 1345-1351.
Tremblay, J., Prakash. A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon S., and Birchfield, S. (2018). “Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, Utah, 969-977.
Turchenko, V., Chalmers, E., Luczak, A. (2017). “A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe.” Computing Research Repository (CoRR), Available: https://arxiv.org/abs/1701.04949.
Turkan, Y., Bosché, F., Haas, C. T., and Haas, R. (2012). "Automated progress tracking using 4D schedule and 3D sensing technologies." Automation in Construction, 22, 414-421.
Turkan, Y., Bosché, F., Haas, C. T., and Haas, R. (2013). "Toward Automated Earned Value Tracking Using 3D Imaging Tools." Journal of Construction Engineering and Management, 139(4), 423-433.
Vartiainen, J., Kallonen, T. and Ikonen, J. (2008), "Barcodes and mobile phones as part of logistic chain in construction industry." 16th International Conference on Software, Telecommunications and Computer Networks, Split, 305-308.
Vazquez, D., Lopez, A.M., Marin, J., Ponsa, D., Geronimo, D. (2014). “Virtual and real world adaptation for pedestrian detection.” IEEE transactions on pattern analysis and machine intelligence, 36(4), 797 - 809.
Vrat, P. (2014). “Materials Management: An Integrated Systems Approach.” Springer India, New Delhi, India.
Voulodimos, A., Doulamis, N., Doulamis, A., and Protopapadakis, E. (2018). “Deep learning for computer vision: A brief review.” Computational Intelligence and Neuroscience, vol. 2018, 13 pages.
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. (2004). “Image quality assessment: From error visibility to structural similarity.” IEEE Transactions on Image Processing, 13(4), 600–612.
Wang, C., Wu, H., and Tzeng, N. F. (2007). “RFID-Based 3-D Positioning Schemes, INFOCOM 2007.” 26th IEEE International Conference on Computer Communications. IEEE, 1235-1243.
Wang, L.C. (2008). “Enhancing construction quality inspection and management using RFID technology.” Automation in Construction, 17, 467-479.
Wang, X., Truijens, M., Hou, L., Wang, Y., and Zhou, Y. (2014). “Integrating Augmented Reality with Building Information Modeling: Onsite construction process controlling for liquefied natural gas industry.” Automation in Construction, 40, 96–105.
Wang, Z., Hu, H., and Zhou, W. (2017). “RFID enabled knowledge-based precast construction supply chain.” computer-aided civil and infrastructure, Engineering, 1–16.
Wickramatillake, C. D., Lenny Koh, S. C., Gunasekaran, A., and Arunachalam, S. (2007). "Measuring performance within the supply chain of a large scale project." Supply Chain Management: An International Journal, 12(1), 52 – 59.
Won, D., Park M.-W., and Chi, S. (2018). "Construction Resource Localization Based on UAV-RFID Platform Using Machine Learning Algorithm." IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, 2018, 1086-1090.
Wong, E. T. T., and Norman, G. (1997). “Economic Evaluation of Materials Planning Systems for Construction. Construction Management and Economics.” 15(1), 39-47.
Wu, Y., and Kim, H. (2004). “Digital imaging in assessment of construction project progress.” Proceedings of the 21st ISARC, Jeju, South Korea.
Wu, Y., Kim, H., Kim, C., and Han, S. H. (2010). "Object Recognition in Construction-Site Images Using 3D CAD-Based Filtering." Journal of Computing in Civil Engineering, 24(1), 56-64.
Wu, W., Yang, H., Chew, A.S., Yang, S.H., Gibb, G.F., and Li, Q. (2010). “Towards an autonomous real-time tracking system of near-miss accidents on construction sites.” Journal of Automation in Construction, 19, 134-141.
Yang, J., Arif, O., Vela, P. A., Teizer, J., and Shi, Z. (2010). “Tracking multiple workers on construction sites using video cameras.” Advanced Engineering Informatics, 24(4), 428–434.
Yang, J., Park, M. W., Vela, P. A., and Golparvar-Fard, M. (2015). "Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future." Advanced Engineering Informatics, 29(2), 211-224.
Yang, J., Shi, Z.K., and Wu, Z.Y. (2016), “Towards automatic generation of as-built BIM: 3D building facade modeling and material recognition from images." International Journal of Automation and Computing, 13(4), 338-349.
Yeo, K. T., and Ning, J. H. (2002). “Integrating supply chain and critical chain concepts in engineer–procure–construct (EPC) projects.” Int. Journal of Project Management 20(4), 253–262.
Ying, H. Q., and Lee, S. (2019). “A Mask R-CNN Based Approach to Automatically Construct As-is IFC BIM Objects from Digital Images.” In Proceedings of the 36th ISARC, Banff, Alberta, Canada, 764-771.
Young, D., Haas, C., Goodrum, P., and Caldas, C. (2011). "Improving Construction Supply Network Visibility by Using Automated Materials Locating and Tracking Technology." Journal of Construction Engineering and Management, 137(11), 976-984.
Zdziarski, Z. (2018). "The reasons behind the recent growth of computer vision." Retrieved February/10, 2019, Available: http://zbigatron.com/the-reasons-behind-the-recent-growth-of-computer-vision/.
Zaher, M., D. Greenwood, and Marzouk, M. (2018). “Mobile augmented reality applications for construction projects.” Construction Innovation, 18(2), 152–166.
Zhang, J., Shan, S., Kan, M., Chen, X. (2014). "Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment." European Conference on Computer Vision (ECCV), Zurich, Switzerland, 1-16.
Zhang, S., Bogus, S. M., Lippitt, C. D., and Sprague, J. E. (2018). “Geospatial Technologies for Collecting Construction Material Information.” Construction Research Congress, 2018, 660-669.
Zhang, X., Bakis, N., Lukins, T. C., Ibrahim, Y. M., Wu, S., Kagioglou, M., Aouad, G., Kaka, A. P., and Trucco, E. (2009). "Automating progress measurement of construction projects." Automation in Construction, 18(3), 294-301.
Zhu, Z., and Brilakis, I. (2010). “Concrete column recognition in images and videos.” Journal of Computing in Civil Engineering, 24 (6).
Zollmann, S., Hoppe, C., Kluckner, S., Poglitsch, C., Bischof, H., and Reitmayr, G. (2014). “Augmented reality for construction site monitoring and documentation.” Proceedings of the IEEE 102 (2), 137–154.
Zou, J., and Kim, H. (2007). “Using hue, saturation, and value color space for hydraulic excavator idle time analysis.” Journal of Computing in Civil Engineering, 21, 238–246.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top