Browsing by Author "Gao, Xinghua"
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- A Cost-Effective, Scalable, and Portable IoT Data Infrastructure for Indoor Environment SensingAnik, Sheik; Gao, Xinghua; Meng, Na; Agee, Philip; McCoy, Andrew P. (2022-05-15)The vast number of facility management systems, home automation systems, and the ever-increasing number of Internet of Things (IoT) devices are in constant need of environmental monitoring. Indoor environment data can be utilized to improve indoor facilities and better occupants’ working and living experience, however, such data are scarce because many existing facility monitoring technologies are expensive and proprietary for certain building systems. With the aim of addressing the indoor environment data availability issue, the authors designed and prototyped a cost-effective, distributed, scalable, and portable indoor environmental data collection system, Building Data Lite (BDL). BDL is based on Raspberry Pi computers and multiple changeable arrays of sensors, such as sensors of temperature, humidity, light, motion, sound, vibration, and multiple types of gases. The system includes a distributed sensing network and a centralized server. The server provides a web-based graphical user interface that enables users to access the collected data over the Internet. To evaluate the BDL system’s functionality, cost effectiveness, scalability, and portability, the research team conducted a case study in an affordable housing community where the system prototype is deployed to 12 households. The results indicate that the system is functioning as designed, costs $73 per zone and provides 12 types of indoor environment data, is easy to scale up, and is fully portable. This research contributes to the body of knowledge by proposing an innovative way for establishing a distributed wireless IoT data infrastructure for indoor environment sensing in new or existing buildings.
- Graph-Based Simulation for Cyber-Physical Attacks on Smart BuildingsAgarwal, Rahul (Virginia Tech, 2021-06-04)As buildings evolve towards the envisioned smart building paradigm, smart buildings' cyber-security issues and physical security issues are mingling. Although research studies have been conducted to detect and prevent physical (or cyber) intrusions to smart building systems(SBS), it is still unknown (1) how one type of intrusion facilitates the other, and (2) how such synergic attacks compromise the security protection of whole systems. To investigate both research questions, the author proposes a graph-based testbed to simulate cyber-physical attacks on smart buildings. The testbed models both cyber and physical accesses of a smart building in an integrated graph, and simulates diverse cyber-physical attacks to assess their synergic impacts on the building and its systems. In this thesis, the author presents the testbed design and the developed prototype, SHSIM. An experiment is conducted to simulate attacks on multiple smart home designs and to demonstrate the functions and feasibility of the proposed simulation system.
- Intelligence Complements from the Built Environment: A Review of CPS-Enabled Smart Buildings for Cognitively Declined OccupantsAlimoradi, Saeid; Gao, Xinghua (2022-01-01)Traditionally, caregivers, whether formal or informal, have taken the responsibility of providing assistance and care to the patients with cognitive decline. However, both the caregivers and the patients are subjected to experience financial and emotional burdens, which has impacted the patients’ life quality and quality of the provided care. To overcome the situation, Ambient Assistive Living (AAL) technologies have been sought for to replace the caregivers and complement patients’ lack of intelligence. Technologies such as Internet of Things (IoT) and Artificial Intelligence (AI) have enabled intelligent ubiquitous learning for smart buildings to monitor the cognitively declined occupants and provide in-home assistive services and solutions. This review aims to evaluate and summarize the intelligence complements provided by smart buildings enabled with such capabilities to increase the cognitively declined occupants’ quality of life and autonomy. The review finds that most of the existing contributions are towards learning the occupants’ behavior to identify assistive services and solutions. The identified services are delivered through technological interventions or caregivers. Moreover, key research gaps are identified. The most important is the lack of adequate adoption of technological interventions to fully support the occupants’ autonomy and independence. Other identified gaps include challenges in usability and acceptability, ethical concerns, systems' comprehensiveness, and lacking human- in-the-loop. Lastly, a conceptual framework is proposed to address the gaps as the future research directions in the applications of smart buildings supporting cognitively declined occupants.
- Leveraging Artificial Intelligence for Improving Students' Noticing of Practice during Virtual Site VisitsOlayiwola, Johnson Tumininu (Virginia Tech, 2023-01-11)Complementing the theoretical concepts taught in the classroom with practice has been known to enhance students' contextual understanding of the subject matter. Exposing students to practical knowledge is crucial as employers are expressing discontent with the skills of newly hired graduates. In construction education, site visits have been identified as one of the most effective tools to support theory with practice. While site visits allow students to observe construction projects and engage with field personnel, numerous barriers limit its use as an effective educational tool. For instance, there are safety, cost, schedule, and weather constraints, in addition to the logistics of accommodating large class sizes. As a result, instructors employ videos of construction projects as an alternative to physical site visits. However, videos alone are insufficient to draw students' attention to essential practice concepts. Annotations can be used to attract students' attention to practical knowledge while reducing distractions and assumptions. Leveraging on the recent progress in computer vision techniques, this study presents an AI-annotated video learning tool that instructors can utilize to equip students with practice knowledge when there is limited access to physical construction sites. First, this study investigated the construction practice concepts that industry practitioners would want students to know when engaging them in site visits. Afterward, the design and development of the AI-annotated learning tool were guided by the identified practice concepts, cognitive theory of multimedia learning, and dual coding theory. To determine if the learning tool can call students' attention to annotated practice concepts in videos, a usability evaluation was conducted. Finally, this research investigated the influence of individual differences that could contribute to how learners notice practice concepts in videos. This study contributes to the body of knowledge by identifying what construction professionals notice about their work and what they would like students to notice about construction practice. This study reveals that annotations of learning contents in construction videos can direct students' focus to the annotated contents, thereby contributing to the cognitive theory of multimedia learning and dual coding theory. By leveraging machine learning classification algorithms, this research identified the extent to which individual differences such as gender, academic program, and cognitive load can be detected from the ways students notice information in construction videos. Results from this research provide opportunities for researchers to further advance the potential of annotated videos in the construction domain and other fields that employ video as a learning tool.
- Sustainable Operations: A Systematic Operational Performance Evaluation Framework for Public-Private Partnership Transportation Infrastructure ProjectsDu, Juan; Wang, Wenxin; Gao, Xinghua; Hu, Min; Jiang, Haili (MDPI, 2023-05-12)With the application of public–private partnership (PPP) model in urban transportation infrastructure projects, various participants have put forward multi-dimensional demands to the operation and maintenance of infrastructures. This study aims to establish a systematic operational performance evaluation framework for PPP transportation infrastructure projects. Based on a literature review, the balanced scorecard was improved, and a conceptual model of multidimensional performance assessment was constructed. The structure of the qualitative performance assessment system was quantified and analyzed by combining structural equation modeling with questionnaires to obtain causal relationships among the indicators. Subsequently, a system dynamics model was constructed to assess the performance dynamically, and a validation analysis was conducted. It finds that maintaining a low level of operational quality over an extended period can significantly reduce stakeholder satisfaction, consequently exacerbating the decline in project performance. In contrast, an improvement in the level of informatization is found to positively contribute to enhancing operational quality and facilitating the long-term sustainability of project operations. It innovatively integrates four dimensions of financial, multi-stakeholder satisfaction, operation and maintenance quality, and sustainability performance to enrich the theoretical system of PPP transportation infrastructure performance assessment. At the same time, it analyzes the influence mechanism among the indicators and its long-term dynamic performance, which provides an effective decision-making tool for operational performance management.
- Understanding Underlying Risks and Socio-technical Challenges of Human-Wearable Robot Interaction in the Construction IndustryGonsalves, Nihar James (Virginia Tech, 2023-07-06)The construction industry, one of the largest employers of labor in the United States, has long suffered from health and safety issues relating to work-related musculoskeletal disorders. Back-related injuries are one of the most prevalent of all musculoskeletal disorders in the construction industry. Due to advancements in the field of wearable technologies, wearable robots such as passive back-support exoskeletons have emerged as a possible solution. Exoskeletons have the potential to augment human capacity, support non-neutral work positions, and reduce muscle fatigue and physical exertion. Current research efforts to evaluate the potential of exoskeletons in other industry sectors have been focused on outcome measures such as muscle activity, productivity, perceived discomfort and exertion, usability, and stakeholders' perspectives. However, there is scarce evidence regarding the efficacy of using exoskeletons for construction work. Furthermore, the risks and sociotechnical challenges of employing exoskeletons on construction sites are not well documented. Thus, through the lens of human-centric and socio-technical considerations, this study explores the prospects of adopting back-support exoskeletons in the construction industry. Firstly, a laboratory experiment was conducted to quantify the impact of using a passive exoskeleton for construction work in terms of muscle activity, perceived discomfort, and productivity. In order to investigate the acceptance of exoskeletons among construction workers and the challenges of adopting exoskeletons on construction sites, field explorations evaluating usability, perceived discomfort and exertion, social influence, and workers user perceptions were executed. Using sequential mixed methods approach, the stakeholders and factors (i.e., facilitators and barriers) critical for the adoption of exoskeletons on construction sites were investigated. Thereafter, by employing the factors and leveraging the constructs of the normalization process theory, an implementation plan to facilitate the adoption of passive exoskeletons was developed. The study contributes to the scarce body of knowledge regarding the extent to which exoskeletons can reduce ergonomic exposures associated with construction work. This study provides evidence of the perceptions of the contextual use of wearable robots, and workers' interaction with wearable robots on construction sites. The study contributes to the normalization process theory by showing its efficacy for the development and evaluation of implementation frameworks for construction industry. Furthermore, this study advances the socio-technical systems theory by incorporating all its subsystems (i.e., human, technology, organization and social) for investigating the potential of using a passive back support exoskeleton in the construction industry.
- Unmanned Aerial Manipulators in Construction - Opportunities and ChallengesNagori, Chinmay (Virginia Tech, 2020-12-23)Unmanned Aerial Vehicles (UAVs) have now been accepted as an alternative medium to human workers for data collection processes in various industries. The capabilities of UAVs are now being extended from passive tasks of data collection to active tasks of interacting with the environment by equipping them with robotic arms and function as Unmanned Aerial Manipulators (UAMs). Research on Unmanned Aerial Manipulators has been growing in the last few years. The applications of UAMs in terms of sensor installation, inspections, door opening, valve turning, pick and drop, etc. have been studied for the oil and gas industry, and civil applications, etc. However, there is a lack of studies in understanding applications of UAMs and their capabilities in construction and in advancing construction activities. The goal of this research is to identify potential opportunities and challenges of the application of UAM in construction projects. The study will undertake an extensive literature review and semi-structured interviews with industry experts to address research questions. This study will have a significant contribution to the introduction and development of new contact-based UAV-guided technologies in construction.
- Work Order Prioritization Using Neural Networks to Improve Building OperationEnsafi, Mahnaz (Virginia Tech, 2022-10-20)Facility management involves a variety of processes with a large amount of data for managing and maintaining facilities. Processing and prioritizing work orders constitute a big part of facility management, given the large number of work orders submitted daily. Current practices for prioritizing work orders are mainly user-driven and lack consistency in collecting, processing, and managing a large amount of data. Decision-making methods have been used to address challenges such as inconsistency. However, they have challenges, including variations between comparisons during the actual prioritization task as opposed to those outside of the maintenance context. Data-driven methods can help bridge the gap by extracting meaningful and valuable information and patterns to support future decision-makings. Through a review of the literature, interviews, and survey questionnaires, this research explored different industry practices in various facilities and identified challenges and gaps with existing practices. Challenges include inconsistency in data collection and prioritizing work orders, lack of data requirements, and coping strategies and biases. The collected data showed the list of criteria and their rankings for different facilities and demonstrated the possible impact of facility type, size, and years of experience on criteria selection and ranking. Based on the results, this research proposed a methodology to automate the process of prioritizing work orders using Neural Networks. The research analyzed the work order data obtained from an educational facility, explained data cleaning and preprocessing approaches, and provided insights. The data exploration and preprocessing revealed challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for work orders' execution, and lack of correlation between collected criteria and the schedule. Through hyperparameter tuning, the optimum neural network configuration was identified. The developed neural network predicts the schedule of new work orders based on the existing data. The outcome of this research can be used to develop requirements and guidelines for collecting and processing work order data, improve the accuracy of work order scheduling, and increase the efficiency of existing practices using data-driven approaches.