The problem of using a computer to control the figuring of an optical surface is investigated. By assuming a linear, shift invariant figuring process, the amount of material removed during each figuring run can be computed. This is done by convolving a tool removal profile with a dwell function that describes the amount of time the figuring tool spends in each area element on the surface. Four methods of computing a dwell function that will best remove the figure errors are described. The advantages of making surface figure measurements using direct wavefront measurement techniques over the interferogram analysis methods used in previous computer controlled figuring machines are also discussed. The design and construction of a computer controlled optical figuring machine is then reviewed. The machine uses a computer controlled heterodyne interferometer to provide optical testing data on the surface being polished. Two microcomputers are used to analyze the test data and run the machine. Optical figuring is performed by scanning a polishing head with a known removal function over the surface at a rate derived from the surface errors. The operation of the software that computes the run path data and controls the machine hardware is outlined. The performance of each of the machine components is evaluated by comparing the behavior predicted by theory to the measured behavior. Initially, the accuracy of the interferometer is measured. The interferometer is then used to determine the performance of the polishing head by measuring the tool removal function. It is then shown that the machine can be run so that a predictable amount of material is removed from the surface. Finally, the feedback loop is closed and surface figure data from the interferometer is used to correctly polish the central region of a 16 inch diameter mirror. It is shown that the surface figure can be predicted with good accuracy over the entire surface. This work concludes with recommendations for improving the machine hardware and for improving the figuring performance near the edge of the surface being polished.

Construction progress monitoring allows schedule and/or cost deviations to be identified early enough to effectively implement corrective actions. At least 77% of transportation projects experience cost overruns, and as much as 75% of these overruns have been attributed to “real” construction management factors like progress monitoring. Progress is measured on road construction sites in terms of completion percentages at various activity and work package levels. This percentage is then used to identify schedule deviations and support the earned value analysis often used as the baseline for contractor progress payments. Unfortunately, the current methods for producing these completion percentages are not as correct or time efficient as they should be to enable effective project control. The objective of this research is to develop, test, and validate a novel solution for automatically producing completion percentages and progress status determinations that are more correct and time efficient than those generated in current practice. The proposed solution seeks to automatically detect incremental progress on road design layers in 3D as-built point cloud data generated using unmanned aerial photogrammetry and a novel data simulation approach. A parallel as-planned progress estimate is also automatically prepared using 4D information, and the progress status determinations are made by comparing the two results. This solution was tested on 15 datasets (13 simulated and 2 real-world) representing a variety of road designs and progress conditions. The method achieved an average 95% F1 score in layer detection on the real-world data, and mostly outperformed current practice in correctness. The automated processing of as-built and as-planned data to produce the progress estimate took 12 seconds for the real world data, which was indeed faster than the current practice equivalent. Although the research objectives were met, there remains room for further improvement, particularly in regards to the solution’s robustness to occlusions on the monitored surfaces.

This work develops optimal assembly sequences for modular building blocks. The underlying concept is that an automated device could take a virtual shape such as a CAD file, and automatically decide how to physically build the shape using simple, identical building blocks. This entails deciding where to place blocks inside the shape and generating an efficient assembly sequence that a robot could use to build the shape. The blocks are defined in a general, parameterized manner such that the model can be easily modified in the future. The primary focus of this work is the development of methods for generating assembly sequences in a time-feasible manner that ensure static stability at each step of the assembly. Most existing research focuses on complete enumeration of every possible assembly sequence and evaluation of many possible sequences. This, however, is not practical for systems with a large number of parts for two reasons: (1) the number of possible assembly sequences is exponential in the number of parts, and (2) each static stability test is very time-consuming. The approach proposed here is to develop a multi-hierarchical rule-based approach to assembly sequences. This is accomplished by formalizing and justifying both high-level and mid-level assembly rules based on static considerations. Application of these rules helps develop assembly sequences rapidly. The assembly sequence is developed in a time-feasible manner according to the geometry of the structure, rather than evaluating statics along the way. This work only evaluates the static stability of each step of the assembly once. The behavior of the various rules is observed both numerically and through theory, and guidelines are developed to suggest which rules to apply. A secondary focus of this work is to introduce methods by which the inside of the structure can be optimized. This structure optimization research is implemented by genetic algorithms that solve the multi-objective optimization problem in two dimensions, and can be extended to three dimensions.

The world of automation has grown rapidly for the last four decades and it is driven by higher demands from users, technology development and maturity of technologies of industrial processes. Companies all over the world have automated their manufacturing processes which have led to billions of dollars in productivity and quality improvements. In the construction industry innovation transpires extremely slowly and a reluctance to implement new strategies and a low research and development budget also contribute to the slow innovation rate. Because of this, technical innovations such as automation solutions are uncommon in the construction industry. Methods and concepts from the manufacturing industry, such as lean principles and mass customization, have for some time tried to be implemented the construction industry to reach higher productivity. However, despite those efforts, automation solutions are not yet commonplace on the average construction site. The purpose of this thesis is to investigate existing and emerging automation solutions that could be used to automate on-site construction operations with the use of digital technologies. Empirical and theoretical studies were conducted. A literature study was conducted to gain knowledge regarding the construction industry and its digitalization and general level of automation. This was then complemented with an interview study, where Skanska AB’s Technical Specialist was one of the interviewees, in addition to being the company’s mentor for this thesis. Skanska served as an example of a representative construction company since it is one of the largest construction companies in the world. To widen the data collection, the interview study included several relevant companies where their representatives were interviewed regarding their own state of digitalization and automation. For guiding the purpose of the thesis, three research questions were formulated. The research for answering these questions led to the resulting findings regarding what is possible to automate on the construction site, what the benefits could be from implementing such solutions and what the challenges are which needs to be overcome. The results of these studies show that the construction industry is facing challenges regarding the implementation of automation solutions. Examples of this are the lack of data in general, safety concerns and project planning operations. The currently existing industrial robots, for example, are generally heavy with low lifting capacity to weight ratio which is not much of a problem in a manufacturing setting, but it makes for an imperfect fit in the construction industry, since movability and high lifting capacity is of interest for such implementations. This thesis provides knowledge of available technologies that can be used for implementing automation at the construction site as well as what benefits can be expected from successful implementations of such solutions, such as higher productivity, increased profitability and increased safety for both equipment and personnel.

Roadway construction workers have to work in close proximity to construction equipment as well as high-speed traffic, exposing them to an elevated risk of collisions. This research aims to develop an innovative holistic solution to reduce the risk of collisions at roadway work zones. To this end, a connected hazard detection and prevention system is developed to detect potential unsafe proximities in highway work zones and provide warning and instructions of imminent threats. This connected system collects real-time information from all the actors inside and outside of the work zone and communicates it with a cloud server. A hazard detection algorithm is developed to identify potential proximity hazards between workers and connected/automated vehicles (CAV) and/or construction equipment. Detected imminent threats are communicated to in-danger workers and/or drivers. The trajectories and safety status of each actor is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based situational awareness tool, in real-time. To assure the accuracy of hazard detection, the algorithm accommodates various parameters including variant threat zones for workers-on-foot, vehicles, and equipment, the direction of movement, workers' distance to the work zone border, shape of road, etc. The designed system is developed and evaluated through various experiments on the Virginia's Smart Roads located at Virginia Tech. Data regarding activities of workers-on-foot was collected during experiments and was used and classified for activity recognition using supervised machine learning methods. A demonstration was held to evaluate the usability of the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system elevates safety of highway construction and maintenance workers at work sites. It also helps managers and inspectors to keep track of the real-time safety status of their work zone actors as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced. / Master of Science / In order to reduce the risk of collisions for roadway construction workers, this research aims to develop an innovative holistic solution at roadway work zones. In this research, a connected hazard detection and prevention system is developed to detect potential collision hazards in highway work zones and generate warning and instructions of imminent threats. This system collects real-time information from all the workers, construction equipment and connected/automated vehicles (CAV) of the work. A hazard detection algorithm is developed to identify potential proximity hazards between them as well as to recognize the activities of workers. The trajectories and safety status of each worker, equipment or vehicle is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based tool, in real-time. A demonstration was held to evaluate the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system helps managers and inspectors to keep track of the real-time safety status of their work zone worker, equipment and vehicles as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced.

Pallets are widely used to efficiently store and handle goods and are often subjected to bending and impact loads. The consequences of structural failure of a loaded pallet can include loss of goods, increased labor and equipment costs, and possible severe or fatal injury to humans. - The pallet industry, which annually consumes nearly 20% of all lumber manufactured in the United States, recognized a need for a rational design methodology, based upon engineering principles, to ensure consistent safety and economy in pallets of any geometry. To satisfy this need a cooperative research project between Virginia Tech, the U. S. Forest Service, and the National Wooden Pallet and Container Association was established. The objective of the project was to develop methods to design pallets for strength, stiffness, and durability. A primary expected benefit of the design methodology is to allow comparison of different pallet designs on a performance basis, without the need for extensive physical testing. This dissertation presents the results of this cooperative research project. The developed methodology was computerized (Pallet Design System (PDS)) and is intended to allow pallet manufactures to obtain estimates of the maximum safe load capacity or the member dimensions required to resist known loads. Additionally, the program produces estimates of the durability and cost-per-use for pallets in specific service environments. PDS is limited in scope to pallets with up to four stringers and a maximum of 15 deckboards. Five different load types and four support modes can be analyzed. These include uniformly distributed and concentrated loads, and racked, stacked, and sling support modes. The techniques for estimating the strength and stiffness are based on matrix structural analysis and classical beam theory. The deckboard-stringer joints are modeled as spring elements, the stiffness of which are based upon characteristics of the fastener. Most fasteners commonly used in pallet construction (i.e. threaded nails or staples) can be analyzed. A probabilistic design technique based on mean value methods was applied in PDS to ensure safety in the resulting designs. The safety index was calibrated to pallet designs associated with warehouse load data. The physical properties of the material are estimated using either a modified clear-wood property approach (ASTM D-245 method), or in·graded testing of pallet lumber. The durability estimates are based upon studies of field data and economic analysis. / Ph. D.

<p>  </p> <p>This research is motivated by the potential of autonomous mobile robots (AMRs) in enhancing safety, productivity, and efficiency in the construction industry. The dynamic and complex nature of construction sites presents significant challenges to AMRs, particularly in localization and mapping – a process where AMRs determine their own position in the environment while creating a map of the surrounding area. These capabilities are crucial for autonomous navigation and task execution but are inadequately addressed by existing solutions, which primarily rely on visual Simultaneous Localization and Mapping (SLAM) methods. These methods are often ineffective in construction sites due to their underlying assumption of a static environment, leading to unreliable outcomes. Therefore, there is a pressing need to enhance the applicability of AMRs in construction by addressing the limitations of current localization and mapping methods in addressing the dynamic nature of construction sites, thereby empowering AMRs to function more effectively and fully realize their potential in the construction industry.</p> <p>The overarching goal of this research is to fulfill this critical need by developing a novel visual SLAM framework that is capable of not only detecting and segmenting diverse dynamic objects in construction environments but also effectively interpreting the semantic structure of the environment. Furthermore, it can efficiently integrate these functionalities into a unified system to provide an improved SLAM solution for dynamic, complex, and unstructured environments. The rationale is that such a SLAM system could effectively address the dynamic nature of construction sites, thereby significantly improving the efficiency and accuracy of robot localization and mapping in the construction working environment. </p> <p>Towards this goal, three specific objectives have been formulated. The first objective is to develop a novel methodology for comprehensive dynamic object segmentation that can support visual SLAM within highly variable construction environments. This novel method integrates class-agnostic objectness masks and motion cues into video object segmentation, thereby significantly improving the identification and segmentation of dynamic objects within construction sites. These dynamic objects present a significant challenge to the reliable operation of AMRs and, by accurately identifying and segmenting them, the accuracy and reliability of SLAM-based localization is expected to greatly improve. The key to this innovative approach involves a four-stage method for dynamic object segmentation, including objectness mask generation, motion saliency estimation, fusion of objectness masks and motion saliency, and bi-directional propagation of the fused mask. Experimental results show that the proposed method achieves a highest of 6.4% improvement for dynamic object segmentation than state-of-the-art methods, as well as lowest localization errors when integrated into visual SLAM system over public dataset. </p> <p>The second objective focuses on developing a flexible, cost-effective method for semantic segmentation of construction images of structural elements. This method harnesses the power of image-level labels and Building Information Modeling (BIM) object data to replace the traditional and often labor-intensive pixel-level annotations. The hypothesis for this objective is that by fusing image-level labels with BIM-derived object information, a segmentation that is competitive with pixel-level annotations while drastically reducing the associated cost and labor intensity can be achieved. The research method involves initializing object location, extracting object information, and incorporating location priors. Extensive experiments indicate the proposed method with simple image-level labels achieves competitive results with the full pixel-level supervisions, but completely remove the need for laborious and expensive pixel-level annotations when adapting networks to unseen environments. </p> <p>The third objective aims to create an efficient integration of dynamic object segmentation and semantic interpretation within a unified visual SLAM framework. It is proposed that a more efficient dynamic object segmentation with adaptively selected frames combined with the leveraging of a semantic floorplan from an as-built BIM would speed up the removal of dynamic objects and enhance localization while reducing the frequency of scene segmentation. The technical approach to achieving this objective is through two major modifications to the classic visual SLAM system: adaptive dynamic object segmentation, and semantic-based feature reliability update. Upon the accomplishment of this objective, an efficient framework is developed that seamlessly integrates dynamic object segmentation and semantic interpretation into a visual SLAM framework. Experiments demonstrate the proposed framework achieves competitive performance over the testing scenarios, with processing time almost halved than the counterpart dynamic SLAM algorithms.</p> <p>In conclusion, this research contributes significantly to the adoption of AMRs in construction by tailoring a visual SLAM framework specifically for dynamic construction sites. Through the integration of dynamic object segmentation and semantic interpretation, it enhances localization accuracy, mapping efficiency, and overall SLAM performance. With broader implications of visual SLAM algorithms such as site inspection in dangerous zones, progress monitoring, and material transportation, the study promises to advance AMR capabilities, marking a significant step towards a new era in construction automation.</p>