Development of an Automated Monitoring and Control System for Construction Projects

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atmire.migration.oldid5587
dc.contributor.advisorRuwanpura, Janaka
dc.contributor.advisorLichti, Derek
dc.contributor.authorMaalek, Reza
dc.contributor.committeememberEl-Sheimy, Naser
dc.contributor.committeememberJergeas, George
dc.contributor.committeememberFapojuwo, Abraham
dc.contributor.committeememberJaselskis, Edward
dc.date.accessioned2017-05-12T16:33:15Z
dc.date.available2017-05-12T16:33:15Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractConstruction project progress monitoring and structural dimensional compliance control are essential for decision makers to identify discrepancies between the planned and the as-built states of a project, and take timely measures where required. In practice, monitoring is performed manually, a time consuming, error-prone and labour intensive task, particularly in large scale projects. Thus, large projects are monitored unsystematically by collecting limited onsite data, restricting the project management team to identify delays, and rework on time. The correct determination of the project’s performance also relies heavily on the correctness and completeness of the collected data during the monitoring process. Hence, site supervisory personnel spend considerable time just to manually control the quality of the manually collected onsite data. Several research studies have aimed to use remote sensing technologies such as LiDAR and cameras to acquire 3D point clouds of building elements to improve the quality of the collected data; however, these studies assume the planned BIM as a priori knowledge to assign the point clouds to their corresponding structural element, which provide inaccurate basis for reporting the as-built status of a project, especially when the planned and the actual differ or the planned model is not available with sufficient detail. Here, using the most up-to-date pattern recognition, robust statistical analysis and mathematical modelling techniques, a new robust approach was formulated, independent from a pre-existing planned model, to automatically generate the as-built model of common structural elements with predominantly planar and linear surfaces directly from the acquired point cloud. In the context of four experiments, ten sets of point clouds, nine from actual construction sites, were collected to express, verify and validate the diverse applicability of the proposed system for automated progress monitoring, structural displacement analysis, and dimensional conformation control. It was demonstrated that the novel robust planar and linear point cloud classification and segmentation method, presented here, achieved an overall accuracy of better than 90.4% for all datasets, indicating its generic applicability for construction projects. It was also shown that the proposed system is capable of automatically generating as-built models of common structural elements with the 3-5mm desired construction grade accuracy.en_US
dc.identifier.citationMaalek, R. (2017). Development of an Automated Monitoring and Control System for Construction Projects (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25664en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25664
dc.identifier.urihttp://hdl.handle.net/11023/3821
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectRemote Sensing
dc.subjectMathematics
dc.subjectStatistics
dc.subjectComputer Science
dc.subjectEngineering--Civil
dc.subject.otherAutomated progress monitoring and dimensional conformance control
dc.subject.otherAutomated 4D as-built model generation
dc.subject.otherRobust statistics
dc.subject.otherAutomated Planar and linear classification and segmentation of point cloud data
dc.subject.otherPrincipal components analysis
dc.subject.otherMinimum covariance determinant
dc.titleDevelopment of an Automated Monitoring and Control System for Construction Projects
dc.typedoctoral thesis
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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