Abstract: Bridge deck inspection is essential task to monitor the health of the bridges. Condition monitoring and timely implementation of maintenance and rehabilitation procedures are needed to reduce future costs associated with bridge management. A number of Nondestructive Evaluation (NDE) technologies are currently used in bridge deck inspection and evaluation, including impact-echo (IE), ground penetrating radar (GPR), electrical resistivity (ER), ultrasonic surface waves (USW) testing, and visual inspection. However, current NDE data collection is manually conducted and thus faces with several problems such as prone to human errors, safety risks due to open traffic, and high cost process. This paper reports the automated data collection and analysis for bridge decks based on our novel robotic system which can autonomously and accurately navigate on the bridge. The developed robotic system can lessen the cost and time of the bridge deck data collection and risks of human inspections. The advanced software is developed to allow the robot to collect visual images and conduct NDE measurements. The image stitching algorithm to build a whole bridge deck image from individual images is presented in detail. The ER, IE and USW data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck, respectively. These condition maps provide detail information of the bridge deck quality. The automated bridge deck data collection and analysis is developed. The image stitching algorithm allowed to generate a very high resolution image of the whole bridge deck, and the bridge viewer software allows to calibrate the stitched image to the bridge coordinate. The corrosion, delamination and elastic modulus maps were built based on ER, IE and USW data collected by the robot to provide easy evaluation and condition monitoring of bridge decks.

Abstract: Rate models for predicting vehicular emissions of nitrogen oxides (NO X ) are insensitive to the vehicle modes of operation, such as cruise, acceleration, deceleration and idle, because these models are usually based on the average trip speed. This study demonstrates the feasibility of using other variables such as vehicle speed, acceleration, load, power and ambient temperature to predict (NO X ) emissions to ensure that the emission inventory is accurate and hence the air quality modelling and management plans are designed and implemented appropriately. We propose to use the non-parametric Boosting-Multivariate Adaptive Regression Splines (B-MARS) algorithm to improve the accuracy of the Multivariate Adaptive Regression Splines (MARS) modelling to effectively predict NO X emissions of vehicles in accordance with on-board measurements and the chassis dynamometer testing. The B-MARS methodology is then applied to the NO X emission estimation. The model approach provides more reliable results of the estimation and offers better predictions of NO X emissions. The results therefore suggest that the B-MARS methodology is a useful and fairly accurate tool for predicting NO X emissions and it may be adopted by regulatory agencies.

Abstract: Taiwan experiences typhoons annually and the heavy rain accompanies a typhoon often causes flooding and damage. Local decision-makers invest heavily in flood-prevention measures thus need to allocate resources wisely to minimize the destruction. As floods periodically cause tremendous damage to lives and property, passing on the learning of previous generations becomes important. Disaster education is therefore critical in the region. However, the current disaster education curriculum follows a traditional education methodology focusing on theoretical and conceptual knowledge and teaching. An alternative methodology is needed to motivate students and to deliver practical experience in disaster education. This study adopted a persuasive technology game-initiated-learning (GIL) and developed a Flood Protection game to understanding the following: (1) students’ motivation for learning, (2) their ability to play the game, and (3) their focus on disaster topics. To evaluate their levels of motivation, students’ reactions were analyzed and categorized into four specific expressions: discussion, question, laughter, and screaming. Students’ game playing ability was analyzed and categorized into four stages: explore, aware, fluent, and known. Finally, the content of students’ discussions and questions were analyzed to assess their interest in disaster education. Students’ levels of motivation were found to be high (an average of 2.3 instances of discussion, 0.7 instances of question, 1.3 instances of laughter, and 0.8 instances of screaming were observed per minute during the game); their ability to play the game was sufficient (79% of students completed the game having reached the “known” stage); and the content of their discussions and questions was closely related to disaster knowledge (73% of the 256 discussions and questions related closely to flood and disaster knowledge). From this research, we find that GIL is a persuasive design in flood protection training. Students with low motivation in conventional learning environments were triggered after playing the game. It enhanced students’ motivation to learn more about flooding. In short, the game does benefit flood disaster education, indicating that a well-designed game may promote students’ motivation in disaster education and cause behavior change.

Abstract: Simulation is a proven technique for effective construction site layout planning and heavy equipment resource optimization. Traditional simulation uses historical data as input for task durations. Data is fed into activity cycle diagrams which many times do not consider any of the rapidly changing spatial constraints that are present on a construction site. Very little research has been conducted towards more realistic, real-time simulation involving data gathering from live actors and documenting the effect of potential changes to such a spatial-temporal work environment. Cell-based simulation, incorporating continuous spatial changes to the site layout during project operations and real-time Global Positioning System (GPS) location tracking data from equipment resources, is introduced for more realistic and rapid modeling. The potential of analyzing and visualizing the effects of spatial consideration of varying resource combinations in earthmoving cycles on productivity and site congestion are explored. It provides insight and awareness in decision making for resource management, site layout and internal traffic control planning. The proposed cell-based simulation system handles complex and more realistic scenarios. Various cell parameters were tested in a case study of common earthmoving operations. The advantages of the cell-based over a traditional simulation model include ease of visualization and simplicity in modeling spatial constraints (e.g., ramp restricting traffic to one-way flow). The system provides full control over the flow of resources by using predefined rules or algorithms. It simplifies the design process since, except for some certain key cells, other ordinary cells followed the same rules without being programmed individually. Future research may involve multiple, non-interacting crews competing for resources and study of time-space conflicts in more detail. The presented cell-based simulation system is able to model and visualize the cyclic activities of earthmoving equipment that occur on a construction site in greater detail than previous simulation methods have done. Using near real-time location data from equipment as input value in the simulation helps construction site project engineers, planners, and managers to improve coordination and monitoring of such construction resources.

Abstract: Deep excavations in urban areas have the potential to cause unfavorable effects on ground stability and nearby structures. Thus, it is necessary to evaluate and monitor the environmental impact during deep excavation construction processes. Generally, construction project teams will set up monitoring instruments to control and monitor the overall environmental status, especially during the construction of retaining walls, main excavations, and when groundwater is involved. Large volumes of monitoring data and project information are typically created as the construction project progresses, making it increasingly difficult to manage them comprehensively. To address the abovementioned issues, this project develops a Building Information Model (BIM)-based monitoring system to integrate and visualize monitoring data for risk assessments during urban deep excavation projects. A BIM can be used to establish a comprehensive model of managing a construction project. The system developed herein can access required data from BIM models, and allow complicated numerical data to be displayed effectively in an easily understandable visual format. It is composed of intelligent building components, which includes data attributes and parametric rules for each object. This system can provide a construction project team with a full monitoring view of the ongoing project, along with functions to integrate the information and display it in various ways to present complex engineering monitoring information quickly and clearly. Testing examples of the developed system on the excavation of the O6 station in the Kaohsiung metro system in Taiwan are presented to illustrate the improvements of safety management for adjacent structures in urban deep excavation projects. The risks and issues affecting the safety of excavation activities and proximal structures during a project can be identified earlier through effective visualization of information in the system, enabling construction project teams to address them promptly and appropriately by performing accurate risk assessments and decision making. This system assists construction project teams in identifying and understanding possible blind spots when attempting to achieve risk assessments during urban deep excavation projects, and further enables the adoption of mitigation measures to reduce risk levels.

Abstract: In virtue of their capability to measure temperature, thermal infrared cameras have been widely used in building diagnostics for detecting heat loss, air leakage, water damage etc. However, the lack of visual details in thermal infrared images makes the complement of visible images a necessity. Therefore, it is often useful to register images of these two modalities for further inspection of architectures. Augmented reality (AR) technology, which supplements the real world with virtual objects, offers an ideal tool for presenting the combined results of thermal infrared and visible images. This paper addresses the problem of registering thermal infrared and visible facade images, which is essential towards developing an AR-based building diagnostics application. A novel quadrilateral feature is devised for this task, which models the shapes of commonly present facade elements, such as windows. The features result from grouping edge line segments with the help of image perspective information, namely, vanishing points. Our method adopts a forward selection algorithm to determine feature correspondences needed for estimating the transformation model. During the formation of the feature correspondence set, the correctness of selected feature correspondences at each step is verified by the quality of the resulting registration, which is based on the ratio of areas between the transformed features and the reference features. Quantitative evaluation of our method shows that registration errors are lower than errors reported in similar studies and registration performance is usable for most tasks in thermographic inspection of building facades.

Abstract: Aging infrastructure in the US has gained quite a bit of attention in the past decade. Being one type of a critical infrastructure, embankment dams in the US require significant investment to upgrade the deteriorated parts. Due to limited budgets, understanding the behavior of structures over time through risk assessment is essential to prioritize dams. During the risk assessment for embankment dams, engineers utilize current and historical data from the design, construction, and operation phases of these structures. The challenge is that during risk assessment, various engineers from different disciplines (e.g., geotechnical, hydraulics) come together, and how they would like to visualize the available datasets changes based on the discipline-specific analyses they need to perform. The objective of this research study is to understand the discipline-specific visualization needs of engineers from US Army Corps of Engineers (USACE) who are involved in risk assessment of embankment dams when they deal with large set of data accumulated since the inception of dams. The requirements were identified through a three-phased research approach including interviews with engineers who are regularly involved in risk assessment processes, a card game and review of standards and published work on risk assessment of embankment dams. This paper provides the findings of research conducted with engineers coming from different disciplines within USACE. Findings comprise discipline-specific visualization requirements of engineers for viewing large datasets, containing static data (e.g. design information) and time-series data (e.g. piezometer data, monument measurements etc.), accumulated since the inception of dams. The findings suggest that the visualization of the dam layout, components and geometry within 3D settings overlaid with sensor data (which could be queried based on engineers’ discipline-specific needs) and data analytics results provide a better flexibility to engineers to understand the risk associated with potential failure modes.

Abstract: Visualization of as-built data may change the future of construction project management to a more efficient area of knowledge if appropriate and easy photography and reconstruction tools would be developed and used by practitioners. Most of the current reconstructed 3D point cloud models use unordered photograph collections to generate 4D as-built models. Some of these photographs are not used in the final model mostly because of either a possible overlap with other photos or some faults in photography procedure. Computation time increases exponentially as the number of photos in a photo collection increases. Therefore, the unstructured processes may reduce the performance of a point-cloud model representation. This work shows how the available application of unordered photograph collections are regularly inefficient by measuring the performance of some important criteria, such as the registration success score and the computation time. The case study is the construction of a gas compressor station. Such industrial projects involve several building and work areas (e.g., substation, control building, and piping area). The construction site covers approximately 20 hectares. The case study was conducted in two stages. In the first stage, preexisting images have been used for image based modelling (IBM). In the second stage, images captured based on a step-by-step photography procedure (SPP) have been used for IBM. IBM performance in the first stage of the case study has been compared with the performance of the second stage by comparing the registration success scores. The IBM in the first stage of the case study results in sparse models, which hardly show the geometry of construction scenes. By contrast, capturing images based on the SPP in the second stage of the case study significantly changed the performance of IBM and increased the registration success score. This study provides an easy applicable on-site photography procedure. By adopting the proposed approach and by training the photographers, the model would be more desirable for application in more construction projects. The application of the SPP in the case study shows a significant improvement in the final reconstructed 3D point cloud model and as-built data visualization criteria.

Abstract: A reasonable management and monitoring of construction projects requires accurate construction schedules. Accuracy depends highly on availability of reliable actual logistics data. Such data contain information about available material, equipment, personnel, updated delivery dates, and other data on site conditions. However, such data is often associated with different types of uncertainties due to infrequent collections, varying transport times, or manual assessments. Nonetheless, consideration of these uncertainties is important for evaluating actual data regarding their impact on the overall construction progress. Currently, the integration of such data into construction schedules is a time-consuming, manual and, thus, error-prone process. Therefore, in practice schedules are not updated as often as they should be. To ease the handling of actual data and their integration into construction schedules, a reactive construction scheduling approach is presented. The approach is structured into four successive steps. To evaluate and systematically analyze uncertain actual data, fuzzy set theory and α-cut method are incorporated. Thus, actual data can be integrated into discrete-event simulation models. These models are used to perform simulation-based sensitivity analyzes, which evaluate impacts on construction schedules. As a result, an actual schedule is generated, such that a target-actual schedule comparison can be performed. If significant deviations or problems are identified, adaption is necessary and a new schedule needs to be generated. Thereby, different restrictions on the target schedule, such as contracted delivery dates, milestones or resource allocation must be considered. To perform this required adaption simulation-based optimization is utilized. To validate the method and show its advantages, an initial construction schedule example is created. The example is extended to incorporate uncertain actual logistics data. The proposed method shows how efficient actual data can be analyzed to update construction schedules. Further, the results show a competitive adaption of invalid construction schedules, such that contracted milestones, or other project objectives can be achieved. The presented reactive construction scheduling method has the ability to improve current treatment of uncertain actual logistics data. This helps construction project managers to improve the management and monitoring of construction projects by reducing the time-consuming, error-prone process of updating inconsistent schedules.

Abstract: The arising trend of building information modeling technology application with innovative and revolutionary approach has changed the workflow and deliberation in A/E/C industry. By considering the condensed schedule of the project, operation safety, and information correctness during design and construction stages, Taiwan High Speed Rail Corporation (THSRC) brings BIM technology into Changhua Station construction project. The accumulated massive information helps engineers to coordinate design information among different expertise (architecture, structure, MEP, etc.) into BIM model at earlier phases. The effectiveness of BIM coordination technology increases the design quality and reduces significantly the conflicts of various interfaces. Three-dimension visualized interface increases the problem solving efficiency. During construction stage, construction information is integrated by the model to enhance fluency of construction process among different construction sectors. Application of BIM coordination technology reduces information gap, enhances construction quality and meets the schedule.

Abstract: In recent years, the population structure in Taiwan has changed so dramatically. Based on concerns of social welfare issues, Taiwanese government began to seek principles for assessment of disability. After seven years of carefully evaluation, the World Health Organization’s International Classification of Functioning, Disability, and Health (Abbreviated to ICF) is officially adopted as Taiwan’s assessment standard while most of the assessment procedures of ICF are sophisticated, and time consuming. In this paper, we propose a sensor based decision supporting system for ICF. Our prototype system aims to reduce the burden of medical staffs, and to assist subjects to perform the assessments. This paper integrate multiple devices including ASUS XtionTM, temperature/acceleration/gyro sensors on Arduino, and Zigbee to measure the mobility of limbs and joints. The subject’s log of assessments is then recorded in the database so that the medical staffs can remote-monitor the co ndition of subjects immediately, and analyze the results later. Additionally, in our system, a user-friendly interface is implemented for the detection of dementia. In this paper, three experiments have been conducted for different purpose. The experiment was conducted to compare the variation between thermometer and our device. Moreover, we invited 20 elders aged for 65 to 80 to use our system and all of them gave positive feedback. Two elders were invited to perform full assessment for dementia and the results show that both of them didn’t have sign of dementia. Also, the assessment of joint movement was performed by a 67 year-old elder and the result shows that the elder had well physical function and could take care of daily life. The proposed system has potential for aiding users to perform the ICF testing better and provide benefits to medical staffs and society. With current technology, integration between sensor network systems and artificial intelligence approaches will more and more important. We develop a simple interface for user to manipulate and perform the ICF assessment. In addition, the early detection of dementia likely has the potential to provide patients with an increased level of precaution, which may improve quality of life.

Abstract: Demand response (DR) is referred to programs designed to manage and control electric loads. DR represents one of the vital tools utilized in power distribution networks to improve network efficiency. Effective implementation of DR programs delivers operational benefits such as reduced peak demands and relieved overloads, which are essential in a power system with growing penetration of fundamentally intermittent renewable energy sources. This paper presents a visualization tool for optimising DR programs for domestic hot water systems in distribution power networks. The tool accurately models and predicts potential peak demand reductions through direct load control of domestic hot water systems. It employs a multi-layer thermally stratified hot water cylinder model and Monte Carlo simulations to generate hot water load profiles of domestic customers. To meet peak reduction targets set by the tool user, switching programs found via iterative optimizations are applied to hot water systems. The structure and individual components of the tool are described, and case studies are presented. Impacts of different switching programs on customer’s comfort are evaluated and discussed. The visualization tool is designed to recommend optimum DR switching programs for domestic water heating systems. The tool can assess the performance of a DR switching program by estimating potential peak load reductions and customer comfort characterized by the probability of cold showers. A power system operator can use this tool to determine the available domestic water heating load in a controlled area, and predict the potential reduction in peak load.

Abstract: Wayfinding is an exceedingly complicated cognitive process, especially in complex environments such as hospitals, shopping centers and airports. Inhabitants of such large environments can become lost very easily if they are unfamiliar with the environment. Although they may eventually be able to discover the route to a specific destination, interacting with conventional wayfinding aids, such as consulting a map, understanding signs, and asking people for directions, can be very time-consuming. The research presented in this paper developed a customized instrument (questionnaire) with factors identified as influencing the cognitive process of wayfinding, and conducted an explorative study to investigate user experience and requirements of wayfinding in complex environments; in this paper, a hospital was chosen as the context. The results demonstrate that current wayfinding aids are insufficient to support a person's natural navigational behaviors in the environment. Augmented Reality (AR), which is an innovative concept of enabling digital information to be superimposed onto a real view in real time and context, has great potential to supplement current wayfinding aids. Therefore, we also conceived, developed and implemented an AR-based wayfinding system based on the user requirements identified by the aforementioned instrument. The AR-based wayfinding system was partially validated through case studies, which concluded that AR significantly reduced the time and cognition workload of human wayfinding behaviors.

Abstract: Maintaining an up-to-date record of the number, type, location, and condition of high-quantity low-cost roadway assets such as traffic signs is critical to transportation inventory management systems. While, databases such as Google Street View contain street-level images of all traffic signs and are updated regularly, their potential for creating an inventory databases has not been fully explored. The key benefit of such databases is that once traffic signs are detected, their geographic coordinates can also be derived and visualized within the same platform. By leveraging Google Street View images, this paper presents a new system for creating inventories of traffic signs. Using computer vision method, traffic signs are detected and classified into four categories of regulatory, warning, stop, and yield signs by processing images extracted from Google Street View API. Considering the discriminative classification scores from all images that see a sign, the most probable location of each traffic sign is derived and shown on the Google Maps using a dynamic heat map. A data card containing information about location and type of each detected traffic sign is also created. Finally, several data mining interfaces are introduced that allow for better management of the traffic sign inventories. The experiments conducted on 6.2 miles of I-57 and I-74 interstate highways in the U.S. –with an average accuracy of 94.63 % for sign classification– show the potential of the method to provide quick, inexpensive, and automatic access to asset inventory information. Given the reliability in performance shown through experiments and because collecting information from Google Street View imagery is cost-effective, the proposed method has potential to deliver inventory information on traffic signs in a timely fashion and tie into the existing DOT inventory management systems. Such spatio-temporal representations provide DOTs with information on how different types of traffic signs degrade over time and further provides useful condition information necessary for predicting sign replacement plan.

Abstract: Finding the current location of a specific utility or oneself in an unfamiliar facility can be difficult and time consuming. The hypothesis tested in this paper is that using the information contained within Building Information Models (BIM) can increase the accuracy of indoor positioning algorithms using context-aware sensing technology. The presented work demonstrates how the integration of passive Radio Frequency Identification (RFID) tracking technology and Building Information Modeling (BIM) can assist indoor localization for potential applications in facilities management for proactive preventative maintenance. This paper includes (1) developing a framework that utilizes the integration of commercially-available RFID and a building information model; (2) evaluating the framework for real-time resource location tracking within an indoor environment; and (3) developing an algorithm for real-time localization and visualization in a BIM. A prototype application has been developed that simultaneously connects the RFID readers on a maintenance cart, an asset maintenance database and a BIM model. Three multilateralization approaches were compared in the system to use in the algorithm. Testing was conducted in a facility with a corridor that loops around in a rectangle. The goal is to have a system accuracy within 3 m. Results show that fusing BIM with multilateralization techniques for RFID technology can decrease the number of false reads by 64 % versus standalone multilateralization equations. The greatest system accuracy achieved was 1.66 m. Significantly, the results validate the hypothesis that BIM can increase indoor localization accuracy, and show the usefulness of using BIM for indoor localization in addition to real-time visualization.

Abstract: The architecture, engineering and construction (AEC) industry lacks a framework for capturing, managing, and exchanging project, product, and social information over the lifecycle of a building. The current tools have various limitations, such as lack of interoperability, slow to transfer huge building model files, and possibility of data inconsistency. In this paper, we present a cloud-based BIM server framework namely Social BIMCloud that facilitates BIM information exchange through dynamic merging and splitting of building models. The data model of Social BIMCloud is based on but not limited to IFC. The data model of Social BIMCloud was further extended to accommodate social interactions, by studying the formal modes of communication in the AEC industry. An object-based approach to capture and manage social interactions in AEC projects through a BIM-based visual user interface was also developed and demonstrated. Social BIMCloud addresses the issues of inefficient data transfer speed and data inconsistency in a distributed environment by facilitating the storage and partial exchange of integrated nD BIM models. Data interoperability is facilitated through open BIM standards such as IFC and direct integration with construction software. High performance, scalability, fault tolerance, and cost effectiveness are facilitated through data partitioning, data replication strategies, multi-node structures, and pay-per-use tariff systems, respectively, through a cloud-based NoSQL database. The Social BIMCloud framework helps to develop and exchange BIM models, which are rich in project information such as social interactions, cost, and energy analyses. This framework improves the communication efficiency between project participants, leading to better designs and less rework. The information captured by this framework could also be useful to determine important metrics such as industry trends, relationships among project participants, and user requirements.