Browsing by Author "Haas, Carl"

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Algorithms for Geometric Optimization and Enrichment in Industrialized Building Construction
(University of Waterloo, 2021-03-09) Rausch, Christopher; Haas, Carl
The burgeoning use of industrialized building construction, coupled with advances in digital technologies, is unlocking new opportunities to improve the status quo of construction projects being over-budget, delayed and having undesirable quality. Yet there are still several objective barriers that need to be overcome in order to fully realize the full potential of these innovations. Analysis of literature and examples from industry reveal the following notable barriers: (1) geometric optimization methods need to be developed for the stricter dimensional requirements in industrialized construction, (2) methods are needed to preserve model semantics during the process of generating an updated as-built model, (3) semantic enrichment methods are required for the end-of-life stage of industrialized buildings, and (4) there is a need to develop pragmatic approaches for algorithms to ensure they achieve required computational efficiency. The common thread across these examples is the need for developing algorithms to optimize and enrich geometric models. To date, a comprehensive approach paired with pragmatic solutions remains elusive. This research fills this gap by presenting a new approach for algorithm development along with pragmatic implementations for the industrialized building construction sector. Computational algorithms are effective for driving the design, analysis, and optimization of geometric models. As such, this thesis develops new computational algorithms for design, fabrication and assembly, onsite construction, and end-of-life stages of industrialized buildings. A common theme throughout this work is the development and comparison of varied algorithmic approaches (i.e., exact vs. approximate solutions) to see which is optimal for a given process. This is implemented in the following ways. First, a probabilistic method is used to simulate the accumulation of dimensional tolerances in order to optimize geometric models during design. Second, a series of exact and approximate algorithms are used to optimize the topology of 2D panelized assemblies to minimize material use during fabrication and assembly. Third, a new approach to automatically update geometric models is developed whereby initial model semantics are preserved during the process of generating an as-built model. Finally, a series of algorithms are developed to semantically enrich geometric models to enable industrialized buildings to be disassembled and reused. The developments made in this research form a rational and pragmatic approach to addressing the existing challenges faced in industrialized building construction. Such developments are shown not only to be effective in improving the status quo in the industry (i.e., improving cost, reducing project duration, and improving quality), but also for facilitating continuous innovation in construction. By way of assessing the potential impact of this work, the proposed algorithms can reduce rework risk during fabrication and assembly (65% rework reduction in the case study for the new tolerance simulation algorithm), reduce waste during manufacturing (11% waste reduction in the case study for the new panel unfolding and nesting algorithms), improve accuracy and automation of as-built model generation (model error reduction from 50.4 mm to 5.7 mm in the case study for the new parametric BIM updating algorithms), reduce lifecycle cost for adapting industrialized buildings (15% reduction in capital costs in the computational building configurator) and reducing lifecycle impacts for reusing structural systems from industrialized buildings (between 54% to 95% reduction in average lifecycle impacts for the approach illustrated in Appendix B). From a computational standpoint, the novelty of the algorithms developed in this research can be described as follows. Complex geometric processes can be codified solely on the innate properties of geometry – that is, by parameterizing geometry and using methods such as combinatorial optimization, topology can be optimized and semantics can be automatically enriched for building assemblies. Employing the use of functional discretization (whereby continuous variable domains are converted into discrete variable domains) is shown to be highly effective for complex geometric optimization approaches. Finally, the algorithms encapsulate and balance the benefits posed by both parametric and non-parametric schemas, resulting in the ability to achieve both high representational accuracy and semantically rich information (which has previously not been achieved or demonstrated). In summary, this thesis makes several key improvements to industrialized building construction. One of the key findings is that rather than pre-emptively determining the best suited algorithm for a given process or problem, it is often more pragmatic to derive both an exact and approximate solution and then decide which is optimal to use for a given process. Generally, most tasks related to optimizing or enriching geometric models is best solved using approximate methods. To this end, this research presents a series of key techniques that can be followed to improve the temporal performance of algorithms. The new approach for developing computational algorithms and the pragmatic demonstrations for geometric optimization and enrichment are expected to bring the industry forward and solve many of the current barriers it faces.
Analysis of Risks and Cost Overruns in Design-Bid-Build Highway Infrastructure Projects in Ontario
(University of Waterloo, 2017-09-27) Chahrour, Lara; Haas, Carl; Bachmann, Chris
Cost overruns commonly occur in infrastructure projects, and when the owner is a government entity, these overruns may disrupt the funding available for other projects. Research on large projects indicates that actual project costs are on average 20% higher than estimates for road projects and 34% higher than estimates for tunnel and bridge projects. Other studies that reiterate the presence of cost overruns report values between 3.9 and 10 percent. Risk management can be used to identify and assess risks that may cause overruns and develop risk response plans to address them. The objective of this research is to use risk management knowledge to identify and assess project risks and their expected impacts on highway infrastructure projects in Ontario. The studied Ministry of Transportation of Ontario (MTO) projects have an average cost overrun of 5.2% of tender value for new construction projects, and 11.5% for rehabilitation projects. The risk identification and analysis is followed by a comparison between MTO’s risk management experience and other typical North American organizations that are involved in transportation infrastructure such as Infrastructure Ontario and the California Department of Transportation, as well as other contract delivery methods such as design-build and public- private partnerships. From analyzing 986 risk events, this research identifies design scope changes, material, and latent conditions as the main risks that appear to influence cost overruns for rehabilitation projects. For new construction, the main risks are design scope changes, latent conditions, and permits and regulations. Once the risks are identified and analyzed, action is required to manage the risks that are considered most important. This thesis touches lightly on possible risk management actions for the identified risks.
Assessment Methods for Advanced Masonry Work Systems
(University of Waterloo, 2021-03-19) Ryu, JuHyeong; Haas, Carl; Abdel-Rahman, Eihab
The physically strenuous and demanding nature of construction tasks exposes workers to injury risks, can reduce productivity, and contributes to undesirable early retirement. In spite of these risks, human performance in the workplace is often managed by over-simplified standards. Complex construction sites require continuous manual labor intervention. Site complexity also preclude objective and reliable quantification of labor exposure to ergonomic risk factors. It also impedes the introduction of automation and robotics in construction industry despite recent advancements in other construction technologies. The overarching goal of this dissertation is to identify opportunities for human-centric advanced work assessment systems that can 1) objectively and simultaneously evaluate ergonomic risk levels and productivity in construction tasks involving heavy material handling, 2) effectively identify safe and productive working postures and techniques that workers develop as they gain experience, and 3) evaluate the impact of introducing new, semi-automated work systems on health and productivity in a construction context. To achieve these goals, this research adopts wearable inertial measurement unit (IMU) based motion capture systems as means of data collection in construction worksites. It analyzes the resultant whole-body kinematic data using analytical tools including combined biomechanical-productivity analysis, rule-based postural ergonomic risk assessment, statistical analysis, and data clustering algorithms. This research specifically focuses its efforts on the masonry field, one of the most labor-intensive trades in construction. Over the span of four years, 45 masons at various levels of experience participated in field experiments within the framework of this study. The acquired data was used to develop automated ergonomic assessment systems to evaluate risk levels via various rule-based assessment tools as well as biomechanical analysis. This approach enabled us to objectively evaluate ergonomic risk level in construction tasks, then analyze the relationships among body loads, experience, and work methods to quantitatively investigate differences in joint loads between experts and apprentices. Furthermore, motion data-driven identification of expert work technique was proposed as a guide to proper working methods and apprentice training. These approaches allowed us to identify proper work techniques adopted by experts and suggested the utilization of expert' techniques in apprentice training to reduce the prevalence of occupational injuries and to improve productivity. Leveraging these insights, this study proposed a systematic and objective methodology to assess the value of a semi-automated work system in a construction context. The proposed methodology fills an important technology gap by representing a proactive approach for the evaluation of semi-automated work systems in terms of reduction in exposure to health risks and improvements of productivity. Ultimately, the present research seeks to maximize occupational performance by minimizing the level of human efforts in construction.
Automated Pipe Spool Recognition in Cluttered Point Clouds
(University of Waterloo, 2016-05-06) Czerniawski, Thomas; Haas, Carl; Walbridge, Scott
Construction management is inextricably linked to the awareness and control of 3D geometry. Progress tracking, quality assurance/quality control, and the location, movement, and assembly of materials are all critical processes that rely on the ability to monitor 3D geometry. Therefore, advanced capabilities in site metrology and computer vision will be the foundation for the next generation of assessment tools that empower project leaders, planners, and workers. 3D imaging devices enable the capture of the existing geometric conditions of a construction site or a fabricated mechanical or structural assembly objectively, accurately, quickly, and with greater detail and continuity than any manual measurement methods. Within the construction literature, these devices have been applied in systems that compare as-built scans to 3D CAD design files in order to inspect the geometrical compliance of a fabricated assembly to contractually stipulated dtolerances. However, before comparisons of this type can be made, the particular object of interest needs to be isolated from background objects and clutter captured by the indiscriminate 3D imaging device. Thus far, object of interest extraction from cluttered construction data has remained a manual process. This thesis explores the process of automated information extraction in order to improve the availability of information about 3D geometries on construction projects and improve the execution of component inspection, and progress tracking. Specifically, the scope of the research is limited to automatically recognizing and isolating pipe spools from their cluttered point cloud scans. Two approaches are developed and evaluated. The contributions of the work are as follows: (1) A number of challenges involved in applying RANdom SAmple Consensus (RANSAC) to pipe spool recognition are identified. (2) An effective spatial search and pipe spool extraction algorithm based on local data level curvature estimation, density-based clustering, and bag-of-features matching is presented. The algorithm is validated on two case studies and is shown to successfully extract pipe spools from cluttered point clouds and successfully differentiate between the specific pipe spool of interest and other similar pipe spools in the same search space. Finally, (3) the accuracy of curvature estimation using data collected by low-cost range-cameras is tested and the viability of use of low-cost range-cameras for object search, localization, and extraction is critically assessed.
Canadian Construction Automation and Robotics Roadmap
(University of Waterloo, 2025-05-29) Haas, Carl; Olumo, Adama; Hwang, Joon Ha; Docking, Angie; Shahi, Arash; Kim, Joyce; Katsimpalis, Emmanouil
Automation and robotics play a key role in construction productivity gains. A construction automation and robotics R&D roadmap to achieve such gains in Canada is presented in this report. Developed through workshops, practice and literature reviews, analysis, and synthesis, it includes an overall framework for how key Canadian stakeholders may participate in a technology pipeline that creates construction productivity gains through application of automation and robotics. Key knowledge and implementation gaps from the perspective of the construction industry are identified, as well as impediments to implementation of automation and robotics in construction. Suggestions are made for means by which such impediments can be overcome, and recommendations are made for implementation of a construction automation and robotics R&D program that will expedite deployment of solutions at scale.
A Cloud-Based Architecture for BIM as an Asset for Project Management
(University of Waterloo, 2019-08-12) MERY, ELVINA Constance; Haas, Carl
After more than 30 years of research, better solutions continue to be sought for nuclear power plant decommissioning and radioactive waste management. As some approaches are interesting, improvements are still required in order for them to become generalized solutions. This thesis is a part of a larger research project that focuses on developing robotic and automated technologies that could support the decommissioning of the nuclear power plant in Pickering, Ontario. The overarching research project is divided into four main tasks: (i) automatic scanning of parts of a nuclear power plant; (ii) creation of BIMs (Building Information Models) from these scans for integrated asset management, and decommissioning planning and analysis; (iii) non destructive evaluation of elements in the nuclear power plant; and (iv) packing optimization of the radioactive waste for its storage and management. This thesis concerns the second part of the project: creating BIM from the scans (point clouds) generated automatically by a robotic mobile platform. Using Revit® and Recap®, the point clouds are opened in the software and the BIM is created manually from them. A comparison with automatic recognition is made and the limits of both methods are analyzed in order to present the state of the art of automation in this process and the future improvements that can be done. Dividing this larger research project into four tasks is necessary but creates data management problems, representative of the decommissioning planning challenge. In fact all the data is collected separately with no common storage. Because of the size of this project, it appears possibly advantageous to create an interface where all the data can be shared and accessible by all allowed members. However, the confidentiality of some information must be respected. The security aspect of the developed cloud-based interface is introduced in this thesis and its different functions are presented. The working environment programmed here can be utilized as an approach for BIM-based asset and project management. To prepare for future modifications and generalization to other domains or fields of construction, it has the advantage of being customizable. Indeed, all the functions here are coded in Javascript and are designed for this nuclear power plant decommissioning project. But other functionalities can be developed and existing ones can be suppressed to suit perfectly another project.
Construction Scene Point Cloud Acquisition, Object Finding and Clutter Removal in Real Time
(University of Waterloo, 2017-08-15) Sharif, Mohammad-Mahdi; Haas, Carl; West, Jeffrey
Within industrial construction, piping can constitute up to 50% of the cost of a typical project. It has been shown that across the activities involved in pipe fabrication, pipe fitting has the highest impact on the critical path. The pipe fitter is responsible for interpreting the isometric drawing and then performing the tack welds on piping components so that the assembly complies with the design. Three main problems in doing this task are identified as: (1) reading and interpreting the isometric drawing is challenging and error prone for spatially complicated assemblies, (2) in assemblies with tight allowable tolerance, a number of iterations will take place to fit the pipes with compliance to the design. These iterations (rework) will remain unrecorded in the production process, and (3) no continuous measurement tool exists to let the fitter check his/her work in progress against the design information and acceptance specifications. Addressing these problems could substantially improve pipe fitters’ productivity. The objective of this research is to develop a software package integrating a threefold solution to simplify complex tasks involved in pipe fabrication: (1) making design information easier to understand, with the use of a tablet, 3D imaging device and an application software, (2) providing visual feedback on the correctness of fabrication between the design intent and the as-built state, and (3) providing frequent feedback on fabrication using a step-by-step assembly and control framework. The step-by-step framework will reduce the number of required iterations for the pipe fitter. A number of challenges were encountered in order to provide a framework to make real time, visual and frequent feedback. For frequent and visual feedback, a real time 3D data acquisition tool with an acceptable level of accuracy should be adopted. This is due to the speed of fabrication in an industrial facility. The second challenge is to find the object of interest in real time, once a point cloud is acquired, and finally, once the object is found, to optimally remove points that are considered as clutter to improve the visual feedback for the pipe fitters. To address the requirement for a reliable and real time acquisition tool, Chapter 3 explores the capabilities and limitations of low cost range cameras. A commercially available 3D imaging tool was utilized to measure its performance for real time point cloud acquisition. The device was used to inspect two pipe spools altered in size. The acquired point clouds were super-imposed on the BIM (Building Information Model) model of the pipe spools to measure the accuracy of the device. Chapter 4 adapts and examines a real time and automatic object finding algorithm to measure its performance with respect to construction challenges. Then, a K-Nearest Neighbor (KNN) algorithm was employed to classify points as being clutter or corresponding to the object of interest. Chapter 5 investigates the effect of the threshold value “K” in the K-Nearest Neighbor algorithm and optimizing its value for an improved visual feedback. As a result of the work described in this thesis, along with the work of two other master students and a co-op student, a software package was designed and developed. The software package takes advantage of the investigated real time point cloud acquisition device. While the object finding algorithm proved to be effective, a 3-point matching algorithm was used, as it was more intuitive for the users and took less time. The KNN algorithm was utilized to remove clutter points to provide more accurate visual feedback more accurate to the workers.
Derivation of Minimum Required Model for Augmented Reality Based Stepwise Construction Assembly Control
(University of Waterloo, 2018-07-05) Jeanclos, Nicolas; Haas, Carl
The global 3D imaging market is expected to reach $26 billion by 2024 with an annual growth of 23.7% (3D Imaging Market Global Scenario, Market Size, Trend and Forecast, 2015 – 2024. 2018). Various industries are extensively involved in this emergence including the healthcare and entertainment industries, the architecture and construction industries. Additionally, global steel pipe demand is predicted to rise by 3.5% annually until 2019. The combination of the two growths raises the potential of 3D imaging technologies in the construction industry, especially in the piping industry. Thanks to the virtuous cycle between growth and innovation, development and applications of new 3D vision technologies and techniques has become a need for the construction industry facing harsh competition globally. Similarly, prefabrication has been boosted in the construction industry, reducing costs and optimizing time of fabrication. It also copes with the increased demand of small tolerances which sets the industry and its labor under high pressure. Thus, quality control is reinforced in fabrication facilities, and innovations can be deployed in that domain to preclude assemblies from any incompliance. Employing 3D scanners is one effective way to do so, and the recent emergence of handheld laser scanners has created the opportunity to develop efficient new methods to be used for quality control. This thesis proposes a novel methodology for deriving 3D models for assemblies to be fabricated, breaking down a barrier that previously inhibited the utilization of small-range handheld 3D laser scanners. The process is applicable for industrial assembly lines, which present a stepwise fabrication process such as that for pipe spools. The methodology also aims at streamlining the fabrication flow for workers, and can provide as-built information to the management team. To do so, piping assemblies are thoroughly analyzed and decomposed at each and every step around the weld of interest: one part is being added with respect to the other. From this decomposition of a pipe spool, the challenge of the methodology is to shrink down to the minimum the amount of components that have to be investigated to control the geometry of the assembly. The key concept of solid of revolution is introduced and permits the derivation of the Minimum Required Model (MRM). Examples are generated and experiments are conducted to test the effectiveness of the presented method. This is mainly realized by implementing the algorithm within an in-house software, developed along with another PhD student, a master’s student and a co-op student. The software enables the comparison of the acquired scene to the 3D model by segmenting piping components individually, and generating the as-modelled point cloud. Consequently, piping components can directly be segmented within the software, and the MRM can be derived and compared to the expected model. In order to evaluate the efficiency of the method, three criteria are proposed: (1) the level of spatial complexity between the derived Minimum Required Model and the initial 3D model, (2) the capacity to use a handheld scanner with or without the MRM, and finally (3) the accuracy of the comparison between the acquired scan and the 3D model.
Design and Development of a Training System for Manual Handling Tasks in Masonry
(University of Waterloo, 2021-09-24) McFarland, Tasha; Abdel-Rahman, Eihab; Haas, Carl
The construction industry is one of the industries with the highest rates of musculoskeletal disorders (MSDs). Masons are particularly susceptible to overexertion and back injuries due to the physical demands of their jobs. In the past, optoelectronic motion capture has been considered the ‘gold standard’ for motion capture in biomechanics; however, it is often not feasible for onsite data collection. Therefore, most onsite assessment tools in the industry rely on observational techniques of postures to estimate risk that cannot accurately estimate internal joint demands. Advancements in inertial measurement unit (IMU) technology have led to the development of data collection systems comparable to that of the aforementioned ‘gold standard’, thereby enabling the quantification of joint loads and forces on masons in the working environment. Previous research has reported that “technique” during manual handling tasks, such as lifting, can have a large impact on spinal loads. The comparison of expert and novice working techniques reveals that experts use distinct working strategies, which can lead to both lower joint forces and increased productivity. Furthermore, training based on expert work strategies has been shown to reduce exposures to biomechanical risks. Despite frequency of injuries, MSD risks are often under-prioritized in terms of safety training. Researchers emphasize a need to integrate ergonomics training within apprentices’ skill training classes. This thesis focuses on the development of an enhanced training tool and program to reduce MSD risk in apprentice masons. A novel quantitative scoring system was developed to estimate MSD risk based on the peak joint loads of expert masons. This scoring system was integrated into the enhanced training tool to better assess risk based on onsite measurement of joint loads. Furthermore, the movement patterns of novice, apprentice and expert masons were analysed to determine key characteristics of inexpert and expert techniques. These characteristics were compared to high-risk postures in the literature to establish clear postural guidelines, which were then implemented into the enhanced training tool. The tool was designed to provide evidence-based recommendations to improve posture and technique based on kinematic analyses of masons’ movements. User interviews were conducted with masonry instructors to evaluate challenges, needs, and values for the training program. These insights directed the design of the accompanying educational module and overall training program. The training program and tool has the capacity to reduce biomechanical exposures of apprentice masons and increase productivity.
Developing design option assessment methods for high-rise residential building adaptation projects
(University of Waterloo, 2021-04-01) Shahi, Sheida; Haas, Carl; Beesley, Philip
Adapting existing buildings is complex, but it can reduce the ratio of operating-to-embodied energy and the amount of demolition and construction waste. There has been a growing interest in the adaptation of existing buildings over the past decade as a response to changing environmental conditions and resource depletion. A cohesive perspective on project scope definition, design option assessment, tools and techniques for improving building adaptation is demonstrated. A definition framework is developed first, enabling consistent categorization of building adaptation projects. Then, a decision-making framework is presented for supporting generation, evaluation and selection of multiple conceptually orthogonal design options as a basis for future computational design optimization and detailed design. Lastly, a methodology is developed to improve building adaptation design decision-making by considering multiple environmental and financial parameters, using physics-based simulation tools and decision-making frameworks including multi-attribute utility and interactive multi-objective optimization. The combination of frameworks and methodologies presented in this thesis have been demonstrated to be useful in clarifying building adaptation project scope and definition, and early-stage design and feasibility decision-making. This thesis marks a reference for the future development of interactive and computational tools for improving the proliferation and performance of building adaptation projects.
Development of Analysis Tools for the Facilitation of Increased Structural Steel Reuse
(University of Waterloo, 2016-08-11) Yeung, Jamie; Walbridge, Scott; Haas, Carl
Reuse of structural steel can be more attractive than recycling in many cases, if associated costs and risks are lowered, and if externalities are considered. The costs and risks typically associated with steel reuse arise through the unknown capacity of reused components and increased deconstruction activities that may be required to extract the components. Externalities include such aspects as environmental impact, which is commonly accepted as a benefit to reuse as an alternative to recycling. Low-rise structures are particularly attractive for structural steel reuse, as these structures typically lack steel fireproofing, which can be difficult to remove. Low-rise structures also facilitate efficient deconstruction processes. Geometric characterization is demonstrated in this research to have a key role to play in the decision process for each case of potential steel reuse, because it is used to identify unknown in-situ steel sections and assemblies, and it provides necessary input to structural design reliability analysis of reused steel. In this way, geometric characterization can contribute towards lower reuse costs and lower risk of component failure. Its key role is further validated through a series of 3D imaging experiments and associated reliability analyses. Semi-automated and line fitting techniques were utilized to understand the impact of the identification algorithm on the resulting phi factor results. It is concluded that semi-automated geometric characterization can support increased steel reuse through reduced identification costs and improved reliability. A new set of methods and an understanding of their utility in making reuse more attractive through reduced costs and improved reliability is thus contributed. It was identified that low occupancy structures present an opportunity for improving the phi factor comparison between reused and new steel components. This research also contributes towards decision makers’ and society’s understanding of the life cycle impacts of reuse as an alternative to recycling by presenting a streamlined life cycle analysis methodology based primarily on process models. This methodology consists of a comparative life cycle analysis between recycling and reuse of structural steel components. The application of this methodology is demonstrated through its use in a case study. The results of this case study indicate that a significant reduction in some life cycle impact metric values, particularly greenhouse gases, can result from reusing structural steel rather than recycling it. The impact of the methodology developed and the results of this study on reuse decisions will also be influenced by prices placed on air pollutants, greenhouse gases, water, and other impact elements by society and local markets. Current price indexes support recycling as the lower market cost alternative, but relatively small changes to economic conditions could result in reuse being the less expensive alternative.
Digital Twin Framework and Auto-Linking for Management of Legacy Assets
(University of Waterloo, 2021-04-30) Edwards, Chloe; Haas, Carl
The integration of Digital Twins (DTs) for facility and asset management is becoming increasingly common with the advancement of sensor and modelling technology. A digital twin is defined as a responsive and virtual representation of a physical system or sub-system. While traditionally used at the design stage, there are many benefits to incorporating this technology during operation and maintenance of assets. The application area examined in this thesis relates to Nuclear Power Plant (NPP) facilities with focus on legacy assets. Integration of DTs for facility management (FM) of NPP facilities should improve the efficiency of many processes involved in operations and maintenance of assets. DTs are also advantageous in this application due to their use of virtual representation that can be used for training, scenario modelling, and problem solving with minimal asset down time. To streamline the integration of DTs into current FM standard procedures, the development of a comprehensive DT framework that defines levels of DT development within each NPP subsystem, identifies workflows, and details support documents is required. This thesis aims to develop a DT framework that can be applied to varying levels of legacy assets within an NPP. The framework will guide operators and engineers through the decision-making process that is involved when implementing DTs. The presented DT framework incorporates a layered approach to demonstrate the connections between the physical environment and the virtual digital twin. The underlying levels include perception, communication, and action, each of which represent individual processes that contribute to a functional virtual model. This DT framework uses imagery, 3D scanning, operational data, and asset management software in an organized manner to streamline information integration for digital twin construction. Most of the technology and software included in the framework are commonly used in current practices, which reduces the initial cost and training investment. Since the DT framework presented was specifically developed for NPP FM application, the levels of DT were redefined to accommodate existing NPP FM practices. The support documents and workflows were also adapted according to current practices to simplify the employment of this technology. Implementing DTs within the presented application of a legacy NPP requires a high level of effort that includes digitizing records, creating models of equipment and processes, and linking these digital representations in a functional way within the overall framework. Some of this effort can be automated to improve overall efficiency and reduce time efforts. Auto-linking was developed as a tool that automates steps in the process of identifying physical asset labels within an image and 3D scan of a space within the facility including multiple physical assets such as mechanical, electrical, plumbing equipment, valves, instruments and other related assets. Those asset labels, the information they contain, and the 3D point cloud segments and images related to each tag’s asset must be linked to their respective asset’s information in an asset database that is used for NPP asset management. The 3D point cloud segments, images, and digital database serve as part of the foundation for the NPP DT being constructed within the framework presented. The Auto-linking algorithm uses a deep-learning based approach to object (tag) detection, employs optical character recognition (OCR) to read the asset tag’s unique identifier, then sorts this information into a format that directly relates it, via the asset database, to each 3D point cloud of each scene in which the asset exists. Through Auto-linking, the procedure of labelling legacy assets throughout the facility and documenting existing assets for a digital database is accelerated compared to the current manual procedures. This thesis identifies the challenges faced in FM for NPP facilities and legacy assets and demonstrates how and where DTs could be implemented for improved efficiency. The primary contributions of this thesis include the framework and support tools that can be used for some tasks required for implementing DTs for NPP FM, as well as the Auto-linking algorithm for automated asset management database development or enrichment. While these tools were developed specifically for NPP application, other application areas and adaptation for future work is also highlighted throughout the thesis.
Economic and Energy Impacts of Adaptive Reuse Building Construction
(University of Waterloo, 2019-08-29) Chan, Jacky; Bachmann, Chris; Haas, Carl
Adaptive reuse of buildings is an alternative to a building’s end-of-life where A building’s functional life may be extended to serve another purpose. Many studies suggest that adaptive reuse is more sustainable compared to typical demolition and new construction in terms of environmental, social, and economic impacts. However, these claims are qualitative in nature and are limited to economics at the project scale. This thesis quantifies the energy and economic impacts of adaptive reuse building construction in the Region of Waterloo (RoW) in Ontario, Canada. Input-Output (IO) models were developed to study the impacts of adaptive reuse building construction. First, an IO model was developed for Ontario. Then, it was regionalized into a two-region interregional input output (IRIO) model to study the RoW. The building construction industries’ intermediate inputs and final demands were altered in the Ontario IO model to reflect changes in the building construction industries due to changes in the supply and demand of adaptive reuse buildings. A basic scenario represents the situation where only the building’s superstructure and substructure are reused. The basic scenario was then extended to reflect the reuse of internal non-structural components. The IO models examine impacts to gross domestic product (GDP), industry outputs, employment and energy use, and comparisons are drawn between Ontario and the RoW. It was found that adaptive reuse building construction may benefit Ontario’s and the RoW’s economy and reduce energy consumption under certain combinations of changes in intermediate inputs and final demands. The desired domain of adaptive reuse construction, where energy use decreases, while GDP and employment increases, is discerned for both the residential and non-residential building construction industries in Ontario.
Finding Specific Industrial Objects in Point Clouds using Machine Learning and Procedural Scene Generation
(University of Waterloo, 2025-01-06) Lopez Morales, Daniel; Haas, Carl; Narasimhan, Sriram
In the era of Industry 4.0 and the rise of Digital Twins (DT), the demand for enriched point cloud data has grown significantly. Point clouds allow seamless integration into Building Information Modeling (BIM) workflows, offering deeper insights into structures and enhancing the value of documentation, analysis, and asset management processes. However, several persistent challenges limit the current effectiveness of point cloud methods in industrial settings. The first major challenge is the difficulty in identifying specific objects within point clouds. Finding and labeling individual objects in a complex 3D environment is technically demanding and fraught with various issues. Manually processing these point clouds to locate specific objects is labor-intensive, time-consuming, and susceptible to human error. In large-scale industrial environments, the complexity of layouts and the volume of data make these manual methods impractical for efficient and accurate results. The second major challenge lies in the scarcity of industrial point cloud datasets necessary for training machine learning-based segmentation networks. Automating point cloud enrichment through machine learning relies heavily on the availability of high-quality datasets specific to industrial applications. Unfortunately, comprehensive datasets of this kind are either unavailable or proprietary, creating a significant barrier to developing effective segmentation networks. Furthermore, the few current datasets often lack flexibility, being limited only by the areas that have been scanned. This rigidity, combined with the time-consuming process of manually segmenting data, slows down the development and deployment of scalable machine-learning solutions for point cloud segmentation. These limitations highlight the need for more flexible and adaptive solutions to efficiently address object detection, asset tracking, and inventory management in dynamic industrial scenarios. This research addresses these challenges by developing open-access, weight-balanced class datasets specifically designed for 3D point cloud segmentation in industrial environments. The datasets integrate synthetic data with real-world industrial scans, offering a solution to the problem of imbalanced class distributions, which often hinder the accuracy of neural networks. Two methodologies for synthetic datasets were developed, one with random object placement and the second through a procedural generation pipeline, which includes rules for object placement and rules for generating tube structures for industrial elements, filling the scene with various objects of variable geometric features to understand the different effects that make a dataset realistic. This procedural generation technique provides a flexible method for dataset creation that can be adapted for different objects, point cloud scales, point densities, and noise levels. The dataset improves the generalization capabilities of machine learning models, making them more robust in identifying and segmenting objects within industrial settings. The second part of the research presents a methodology for efficiently and accurately identifying specific objects in point cloud scenes and two methodologies for creating open-access industrial datasets designed to train neural networks for segmentation. The first part of the research focuses on the object-finding methodology, which is crucial for multiple applications, including object detection, pose estimation, and asset tracking. Traditional methods struggle with generalization, often failing to differentiate between unique objects and general classes. The proposed methodology for specific object finding utilizes a point transformer network for point cloud segmentation and a fully convolutional geometric features network to enhance geometrical features using color. A key innovation in this process is using a color-based iterative closest point (ICP) algorithm on the output of the fully convolutional geometric features network. This enables precisely matching segmented objects with a point cloud template, ensuring accurate object identification.
Framework for the Strategic Management of Dimensional Variability of Structures in Modular Construction
(University of Waterloo, 2016-09-01) Rausch, Christopher; West, Jeffrey; Haas, Carl
Challenges in construction related to dimensional variability exist because producing components and assemblies that have perfect compliance to dimensions and geometry specified in a design is simply not feasible. The construction industry has traditionally adopted tolerances as a way of mitigating these challenges. But what happens when tolerances are not appropriate for managing dimensional variability? In applications requiring very precise dimensional coordination, such as in modular construction, the use of conventional tolerances is frequently insufficient for managing the impacts of dimensional variability. This is evident from the literature and numerous industry examples. Often, there is a lack of properly understanding the rationale behind tolerances and about how to derive case specific allowances. Literature surrounding the use of tolerances in construction indicates that dimensional variability is often approached in a trial and error manner, waiting for conflicts and challenges to first arise, before developing appropriate solutions. While this is time consuming, non-risk averse, prone to extensive rework and very costly in conventional construction, these issues only intensify in modular construction due to the accumulation of dimensional variability, the geometric complexity of modules, and discrepancy between module production precision and project site dimensional precision. This all points to a need for a systematic and strategic approach for managing dimensional variability in modular construction. This thesis explores dimensional variability management from a holistic construction life cycle viewpoint, examining key project stages (manufacture, fabrication, aggregation, handling, transportation and erection) to identify critical variability sources and proposing adequate strategies to control dimensional variability. The scope of this work relates primarily to the structural system of commercial building modules, based on the assumption that the sequence of production and dimensional variability of building subsystems (mechanical, electrical, plumbing, architectural) hinge upon the dimensional variability of the structure. A novel method for quantifying dimensional variability is developed, which uses 3D imaging by way of laser scanning and building information models to compute deviations between the intent of a geometric design and the actual as-built construction. Novel strategies for managing dimensional variability are also developed, and include adaptation of manufacturing-based principles and practices for use in construction systems. The inspiration and foundation of these new strategies is derived from the original research of Dr. Colin Milberg, who explored how to apply tolerance theory used in manufacturing into civil construction systems. The new techniques developed in this thesis, along with other previous research, demonstrate that there is a clear correlation between manufacturing industries such as aerospace and automotive assembly production, and that of modular construction assembly production. In light of this, there is an opportunity to improve modular construction processes if these manufacturing-based methods can be appropriately implemented. This is the basis for the proposed methodology presented in this thesis. Application of the proposed methodology using case study examples demonstrates that dimensional variability in modular construction should be approached from a holistic viewpoint. Furthermore, it needs to incorporate much more consideration into the key factors and critical sources of variability rather than pursuing the traditional construction approach of developing inefficient trial and error solutions.
Full-Body Inverse Dynamics Using Inertial Measurement Units
(University of Waterloo, 2019-05-01) Diraneyya, Mohsen; Abdulrahman, Eihab; Haas, Carl
Estimating the loads on the human body is crucial in ergonomics, where it is of use in workplace design, task-load assessment, and safety limits establishment. It is also relevant to rehabilitation studies, where it can be used to design programs, activities, and instruments. Estimating these loads requires the collection of data on motion kinematics and external forces during the task or exercise of interest. Traditionally, Optical Motion Capture (OMC) systems and Force Plates (FPs) were commonly used to collect kinematic data and measure Ground Reaction Forces (GRFs). However, this experimental set-up is limited to laboratory settings and small, confined spaces. It also imposes significant instrumentation costs. The availability of wearable Inertial Measurement Units (IMUs) and better signal processing techniques have allowed for the development of effective whole body Inertial Motion Capture (IMC) systems. Inverse dynamic models that use motion kinematics collected from these systems are also being developed. A challenging aspect in this endeavor is the need to estimate GRFs from kinematics, without recourse to FPs, in order to take full advantage of the IMC systems' portability. To overcome this challenge, some models include upper body segments only and solve for joints loads using a top-down approach. Other models consider gait motions and apply a smooth transition assumption relevant only to the gait cycle. The aim of this current research is to continue along this latter line of development by introducing a general purpose full-body inverse dynamics model based on IMC kinematics. This model allows for true system portability, dispensing with the use of FPs and any other equipment confined to in-lab use. This study has developed a whole-body model that determines the net forces and moments in body joints during general motions captured using an IMC system. Further, an anatomical lower-spine model has also been used to estimate the disk contact forces in the lower back and thereby assess the critical loads on the lower back. Using inverse dynamics, the model also estimates total GRF from the kinematic data and breaks it down into right and left GRFs using an optimization approach that minimizes energy expenditure. The model predictions were validated by comparing them to values measured during an experimental pilot study. The results show an excellent prediction of the total vertical GRF, with relative Root-Mean-Square-Error (rRMSE) of less than 2.4 %. The predictions were less accurate for the horizontal components, ranging from 22.5 % to 39.4% for the anterior-posterior direction, mainly because of their smaller amplitudes. The optimization approach for predicting the right and left vertical GRFs performed well for standing and walking tasks, with rRMSE less than 13.0 %. The model was then used to analyze the forces experienced by masons during bricklaying. Static and dynamic estimates of joint loads were compared to understand how movement affects joint loads.
Great Lakes Regional Water Conflict Analyses
(University of Waterloo, 2021-03-25) Payganeh, Sevda; Haas, Carl; Knight, Mark
This research proposes a holistic framework to help understand and mitigate the interrelated and successive conflicts that occur over water resources in the Great Lakes and the rivers flowing into them. Local Canadian governments, in addition to many public and private companies, are heavy water consumers, who extract vast amounts of water from water sources such as the Great Lakes. Moreover, temperature changes, and increasing storm water in the past few decades, added to pollutants such as phosphorous pouring into the Great Lakes from various origins, place more pressure on these valuable, yet vulnerable water sources. Various NGOs and the states and provinces surrounding the Great Lakes strive to protect the Great Lakes from excessive water extractions and pollutants. The different priorities of the aforementioned stakeholders have become sources of various disputes. Traditional conflict resolution publications tend to focus on investigating each of the conflicts independently from the other disputes existing among the stakeholders. However, a holistic view is required to understand the conflicts, acknowledging the previous disputes, which have transpired in the past when analyzing each conflict. This broader perspective approach presents a better ability to study potential future conflicts, since it enhances the predictability of the scenarios, which might occur later during other disputes. In the first step, after identifying the relevant stakeholders associated with the Great Lakes, conflicts among them are analyzed using the Graph Model for Conflict Resolution (GMCR) approach. However, the input for each conflict's GMCR model is highly influenced by the previous conflicts' outputs. Modeling and analyzing this influence are accomplished through intricately assessing the results of the previous conflicts' GMCRs and linking them to the gathered information on the current conflict of interest. In the next step, major external variables that affect the current steady-state system are investigated. Political happenings, economic factors, social trends, technological advances, legal changes, and environmental crises are some of the key variables that are investigated. Then, several scenarios based on this external analysis of the system are proposed and utilized for enhancing future decision-making. The aforementioned steps are showcased using three case studies of disputes among the Great Lakes stakeholders. The main studied case is the Lake Erie pollution conflict which is investigated in two instances of 1970s and 2010s. It is concluded in this thesis that if the 1970s dispute had been investigated using the causal loops, GMCR, external analysis, and scenario analysis, the stakeholders, especially local authorities in the Lake Erie watershed, would have been able to make better decisions in the more recent dispute in 2010s. This research with the current holistic framework should also enhance understanding of the interrelated conflicts over essential topics such as financial, health, and environmental concerns caused by pollution (specifically algae blooms) in the Great Lakes and the rivers flowing into them. The developed understanding, in addition to the results of the conducted external analysis, should help decisionmakers, especially water utility providers, who carry a huge responsibility towards millions of water users, predict and prevent potential water disputes with other stakeholders. Although the case studies in this research focus on the Great Lakes and their stakeholders, the proposed framework is applicable in other contexts as well.
Impact Assessment of Construction Supply Chain Risk Changes on Project Time and Cost
(University of Waterloo, 2017-09-11) Ahmed, Hani; Haas, Carl; Hegazy, Tarek
The supply chain plays a key role in the construction industry. The importance of the Construction Supply Chain (CSC) is not less than that of the onsite construction phases. There are many risk factors that influence the progress of a supply chain, and it is problematic when the probabilities and impacts of these risk factors are not well defined. While many approaches have been tried in studying SCs in construction, including risk effects, no study has addressed the dynamic updating of the probability of risk events throughout the construction life cycle to effectively study the impact on project time and cost. In order to reduce the impact of unseen risk factors that may affect the progress of any construction project, it is important to have tools to predict the influence of major risk factors in advance. However, risk factors keep changing in their probabilities and impacts along a project’s duration, and these changes will be more severe and have more influence if recognized later rather than earlier, and will be harder and more costly to manage. This research is aimed at helping to recognize the occurrence probabilities of risk factors during the early stages of a project and during the project execution. It aims to build a simulation model that can automatically detect risk factors in construction supply chain, track their changes, quantify their impact on project time and cost. The study starts by identifying typical risk issues related to a CSC that will influence its state. Then, a model for quantifying the amount of risk by defining the probabilities and impacts for each supply chain life cycle is proposed. The main focus of this research is to build models that automatically detect and adjust changes in probabilities for the most severe risk factors associated with CSCs and then estimate their impact on cost and schedule. Four major steps will be completed in the proposed research. First, a real-world industrial construction project is identified. Second, a detailed study on the risk factors associated with the supply chain of the given project is performed. Then, these risk factors are quantified according to the automated detection models for probability change and for studying the impact of each change. Finally, using a Monte Carlo Simulation tool (@Risk), the study will examine the impact of these risk factors on an industrial project to offer a methodology for generating automated reports for project managers to supply them with information on the impacts on the costs and schedules for their projects. The model offer stakeholders involved in a project with a better understanding of the changes in the risk so that throughout the project execution phase, they can take corrective actions and tap off the negative impact of risk on project time and cost. This system can be used for supply chain planning and operation. The power of the system is its ability to respond to various what-if scenarios. The newly introduced model is validated using a real-world industrial project and the details of the project are documented.
Impact of Spatial Cognitive Abilities on the Effectiveness of Augmented Reality in Construction and Fabrication
(University of Waterloo, 2018-09-04) Kwiatek, Caroline; Haas, Carl; Walbridge, Scott
Modular construction has emerged to help address the challenges posed to the construction industry by stagnant productivity rates and a shortage of skilled labor as it allows for greater automation and for work to be completed in a controlled fabrication shop environment as opposed to on a construction site. This requires tighter tolerance controls than traditional stick-built construction because the components must fit together easily with minimal on-site intervention. Modular construction has become widespread in industrial piping construction projects. Pipe spools are assembled in a fabrication shop, installed in a module and then the modules are shipped to the construction site for installation. Since piping components account for up to 50% of the cost of an industrial construction project, it is imperative to assemble these components quickly and correctly. In recent years, augmented reality has become increasingly prevalent as technological advances allow for higher quality digital environments at lower price points. These advances coupled with the increased accessibility of high-quality inexpensive 3D scanning technologies has made it possible to develop augmented reality solutions for real-time conformance control of pipe spool assemblies. An experiment was designed and conducted with the objective of assessing how an augmented reality software can increase productivity and reduce rework in pipe spool assembly. Forty engineers and twenty-one pipe fitters were recruited to assemble a PVC pipe spool using either a two-sided isometric drawing or an augmented reality software. The participants were assessed for the time required to complete the assembly and for the amount of rework they had to complete to create a compliant assembly. They were also surveyed regarding their personal interest in technology and their input on how to best implement this technology. Participants were asked to complete a short test to assess their spatial skills. The results of the completed study show that the use of an augmented reality software can increase productivity and minimize the impact of rework for both expert (pipe fitter) and drawing-literate (engineer) users. The revised workflow created through the usage of such a software essentially eliminates traditional rework. It was found that all users can benefit from the use of such a tool but that users who have lower spatial cognitive skills benefit the most.
Incorporation of Pressure Insoles into Inverse Dynamic Analysis
(University of Waterloo, 2022-01-27) Mahmassani, Ahmad; Abdel-Rahman, Eihab; Haas, Carl
Estimation of body loads during industrial tasks, such as lifting and weight bearing, is central to workplace ergonomics and the study of the safety and risk factors in work techniques. Evaluating those loads requires data collection of body kinematics and the external forces prevailing during the task under evaluation. Current practice calls for kinematic data to be gathered using optical motion capture systems (OMC) and external forces, primarily ground reaction forces (GRFs), to be gathered using force plates. However, this experimental methodology is confined to laboratory settings. Modern motion capture systems, such as those based on Inertial Measurement Units (IMUs), pave the way to more versatile motion analysis techniques not confined to labs. Inverse dynamics models have been developed based on IMU kinematic data. In order to eliminate the need for force plates and to make the experimental apparatus fully portable, those models estimate GRFs from measured accelerations. This study aimed to advance the state-of-the-art on IMU-based inverse dynamics analysis by incorporating pressure insoles as the source of the vertical components of the GRFs, with a view to improving the model fidelity while keeping the experimental apparatus portable. Specifically, it enabled the development of a synchronized and automated inverse dynamics model, comprised of an inertial motion capture suite and pressure insoles, that can estimate net joint forces and moments during manual handling activities. An experiment was designed to examine whether the GRFs measured by the pressure insole can detect and differentiate among various sizes (and weights) of concrete masonry units (CMUs). The instrumented pressure insoles were consistently able to identify three different CMU block weights (8 kg, 16kg, and 24 kg) during various gait patterns (along circular, square, and linear paths). On the other hand, the results were inconclusive in distinguishing between one-handed and two-handed manual handling of CMUs. An improved inverse dynamic model was introduced to calculate the joint loads workers experience during material manual handling based only on measurements by IMU motion capture suits and pressure insoles. The outcome of this thesis was the development of a weight detection algorithm with a detection accuracy of 89% across all three sizes of CMUS as well as an integrated inverse dynamic model incorporating data collected by IMUs motion suits and pressure insoles.