Data-Driven Simulation Modeling of Construction and Infrastructure Operations Using Process Knowledge Discovery

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality

Affecting Fundamental Transformation in Future Construction Work Through Replication of the Master-Apprentice Learning Model in Human-Robot Worker Teams

Construction robots continue to be increasingly deployed on construction sites to assist human workers in various tasks to improve safety, efficiency, and productivity. Due to the recent and ongoing growth in robot capabilities and functionalities, humans and robots are now able to work side-by-side and share workspaces. However, due to inherent safety and trust-related concerns, human-robot collaboration is subject to strict safety standards that require robot motion and forces to be sensitive to proximate human workers. In addition, construction robots are required to perform construction tasks in unstructured and cluttered environments. The tasks are quasi-repetitive, and robots need to handle unexpected circumstances arising from loose tolerances and discrepancies between as-designed and as-built work. It is therefore impractical to pre-program construction robots or apply optimization methods to determine robot motion trajectories for the performance of typical construction work. This research first proposes a new taxonomy for human-robot collaboration on construction sites, which includes five levels: Pre-Programming, Adaptive Manipulation, Imitation Learning, Improvisatory Control, and Full Autonomy, and identifies the gaps existing in knowledge transfer between humans and assisting robots. In an attempt to address the identified gaps, this research focuses on three key studies: enabling construction robots to estimate their pose ubiquitously within the workspace (Pose Estimation), robots learning to perform construction tasks from human workers (Learning from Demonstration), and robots synchronizing their work plans with human collaborators in real-time (Digital Twin). First, this dissertation investigates the use of cameras as a novel sensor system for estimating the pose of large-scale robotic manipulators relative to the job sites. A deep convolutional network human pose estimation algorithm was adapted and fused with sensor-based poses to provide real-time uninterrupted 6-DOF pose estimates of the manipulator’s components. The network was trained with image datasets collected from a robotic excavator in the laboratory and conventional excavators on construction sites. The proposed system yielded an uninterrupted and centimeter-level accuracy pose estimation system for articulated construction robots. Second, this dissertation investigated Robot Learning from Demonstration (LfD) methods to teach robots how to perform quasi-repetitive construction tasks, such as the ceiling tile installation process. LfD methods have the potential to be used in teaching robots specific tasks through human demonstration, such that the robots can then perform the same tasks under different conditions. A visual LfD and a trajectory LfD methods are developed to incorporate the context translation model, Reinforcement Learning method, and generalized cylinders with orientation approach to generate the control policy for the robot to perform the subsequent tasks. The evaluated results in the Gazebo robotics simulator confirm the promise and applicability of the LfD method in teaching robot apprentices to perform quasi-repetitive tasks on construction sites. Third, this dissertation explores a safe working environment for human workers and robots. Robot simulations in online Digital Twins can be used to extend designed construction models, such as BIM (Building Information Models), to the construction phase for real-time monitoring of robot motion planning and control. A bi-directional communication system was developed to bridge robot simulations and physical robots in construction and digital fabrication. Through empirical studies, the high accuracy of the pose synchronization between physical and virtual robots demonstrated the potential for ensuring safety during proximate human-robot co-work.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169666/1/cjliang_1.pd

Towards Smart Earthwork Sites Using Location-based Guidance and Multi-agent Systems

The growing complexity and scope of construction projects is making the coordination and safety of earthwork of a great concern for project and site managers. The difficulty of safeguarding the construction workers is mainly commensurate with the type, scale, and location of the project. In construction operations, where heavy machines are used, various safety and risk issues put the timely completion of a project at stake. Additionally, the construction working environment is heavily susceptible to unforeseen changes and circumstances that could impact the project, both cost and schedule wise. As a response to the looming safety threats or unforeseen changes of working conditions, re-planning is almost always required, in both proactive (preemptive) or reactive (corrective) fashion. In order for re-planning to yield the optimum results, real-time information gathering and processing is a must. Global Positioning System (GPS) and other Real-time Location Systems (RTLSs) have been used for the purpose of real-time data gathering and decision-making in recent years. Similarly, Location-based Guidance Systems (LGSs), e.g., Automated Machine Control/Guidance (AMC/G), have been recently introduced and employed, mainly for the purpose of high-precision earthwork operations. However, currently the application of available LGSs (i.e., AMC/G) is restricted to the machine-level task control and improvement. Also, the high cost of procuring available LGSs, which cost approximately $80,000 for every new piece of equipment, limits the availability of LGSs for small and medium size contractors. Furthermore, the valuable real-time data gathered from various pieces of equipment on site are not effectively utilized to continuously update the simulation models developed at the design phase so that a more realistic view of project progress is available in the execution phase. Finally, despite the growing availability of LGSs, their application for safety is limited to real-time proximity-based object detection and warnings. In view of the ability to control the finest motion of LGS-enabled earthwork equipment, there is a great potential to boost their level of application to the project level, where decisions about the equipment control are made based on the global consideration of a fleet rather that a local view of one single equipment. To the best of the author’s knowledge, a generic methodology that combines real-time data-gathering technologies, LGS and intelligent decision making tools, particularly Multi-agent Systems (MASs), and addresses the safety-sensitive re-planning, is missing. On this premise, this research pursues a methodology which addresses the issue of coordination and safety improvement through the integration of LGSs and MASs. In a nutshell, this research is dedicated to the pursuit of the following objectives: (1) to enable the project-level coordination, monitoring and control through the integration of a MAS architecture and a LGS to help better resolve operational and managerial conflicts; (2) to provide a method for improving the performance of pose estimation based on affordable RTLSs so that LGSs can be applied to a wider scope of older earthwork equipment; (3) to devise a generic framework for Near Real-Time Simulation (NRTS) based on data from LGSs; and (4) to develop a mechanism for improving the safety of earthwork operations using the capabilities of the LGS, NRTS, and MAS. In the proposed framework, every staff member of the project is represented by an exclusive agent in the MASs. More affordable positioning technologies, such as Ultra-Wideband (UWB), are utilized to provide accurate real-time data about the location of machines and workers. An optimization-based method is proposed to consider a set of geometric and operational constraints that govern the behavior of the Data Collectors (DCs) attached to the equipment to improve the equipment pose estimation accuracy. NRTS is used to keep track of the progress of the project and fine-tune the schedule based on the data captured from the site. The agents observe the progress of work executed by their associated equipment, and if any anomalies are detected, viable corrective measures are devised and executed. The inputs to this system are: (a) a stream of real-time data, e.g., location data, flowing from the site, (b) the project design data, and (c) the project progress data and the schedule. Furthermore, a two-layer safety mechanism monitors the safe operation of different pieces of equipment. The first layer of this mechanism enables the equipment to plan a collision-free path considering the predicted movement of all other pieces of equipment. The second layer is acting as a last line of defense in view of possible discrepancies between the predicted paths and actual paths undertaken by the operators. Several prototypes and case studies are developed to demonstrate and verify the feasibility of the proposed framework. It is found that the proposed optimization-based method has a very strong potential to improve the pose estimation using redundancy of more affordable RTLS DCs. Also, the proposed overarching NRTS approach provides a tracking-technology-independent method for processing, analyzing, filtering and visualizing the equipment states that can work with various types of RTLS technologies and under the availability of different levels of sensory data. The proposed safety system is found to provide a balance between economic use of space and the ability to warn against potential collisions in an effective manner using the pose, state, geometry, and speed characteristics of the equipment. Additionally, the safety system demonstrates the ability to provide a reliable basis for the generation of the risk maps of earthwork equipment, using the expected pose and state, and considering the proximity-based and visibility-based risks. The MAS-based framework helps expand the effective domain of LGSs from machine-level guidance to fleet-level coordination. In the view of the presented case studies, the MAS structure is found to be effective in assigning different operations and tasks of a project to the specific agents that will be responsible for their realization. Using a combination of strategic and tactical planning methods, the MAS is able to effectively provide readily executable guidance/control for equipment operators considering a variety of safety issues

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 1

This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) on August 3-5, 1993, and held at JSC Gilruth Recreation Center. SOAR included NASA and USAF programmatic overview, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations. More than 100 technical papers, 17 exhibits, a plenary session, several panel discussions, and several keynote speeches were included in SOAR '93