This research aims to monitor workers’ physiological reactions in virtual construction scenario. With the objective of leveraging affective sensing technology in construction scenario, experiments with Galvanic Skin Response (GSR) was conducted in a 3D simulation developed based on a real construction site. The GSR results obtained from sensor were analyzed in order (i) to assess the feasibility of using virtual environment to generate real emotions, (ii) to examine the relation between questionnaires used to ask people about their experience and their physiological responses and (iii) to identify the factors that affect people’s emotional reactions in virtual environment. Subjects of the experimental group exhibited incoherent responses, as expected in experiments with human subjects. Based on the various reasons for this incoherence obtained from questionnaire part of the experiment, the potential in research for developing training methods with respect to workers’ physiological response capability was identified.

<p>The motivation for this research stems from the promise of coupling multi-sensory systems and advanced data analytics to enhance holistic situational awareness and thus prevent fatal accidents in the construction industry. The construction industry is one of the most dangerous industries in the U.S. and worldwide. Occupational Safety and Health Administration (OSHA) reports that the construction sector employs only 5% of the U.S. workforce, but accounts for 21.1% (1,008 deaths) of the total worker fatalities in 2018. The struck-by accident is one of the leading causes and it alone led to 804 fatalities between 2011 and 2015. A critical contributing factor to struck-by accidents is the lack of holistic situational awareness, attributed to the complex and dynamic nature of the construction environment. In the context of construction site safety, situational awareness consists of three progressive levels: perception – to perceive the status of construction entities on the jobsites, comprehension – to understand the ongoing construction activities and interactions among entities, and projection – to predict the future status of entities on the dynamic jobsites. In this dissertation, holistic situational awareness refers to the achievement at all three levels. It is critical because with the absence of holistic situational awareness, construction workers may not be able to correctly recognize the potential hazards and predict the severe consequences, either of which will pose workers in great danger and may result in construction accidents. While existing studies have been successful, at least partially, in improving the perception of real-time states on construction sites such as locations and movements of jobsite entities, they overlook the capability of understanding the jobsite context and predicting entity behavior (i.e., movement) to develop the holistic situational awareness. This presents a missed opportunity to eliminate construction accidents and save hundreds of lives every year. Therefore, there is a critical need for developing holistic situational awareness of the complex and dynamic construction sites by accurately perceiving states of individual entities, understanding the jobsite contexts, and predicting entity movements.<br></p><p>The overarching goal of this research is to minimize the risk of struck-by accidents on construction jobsite by enhancing the holistic situational awareness of the unstructured and dynamic construction environment through a novel data-driven approach. Towards that end, three fundamental knowledge gaps/challenges have been identified and each of them is addressed in a specific objective in this research.<br></p> <p>The first knowledge gap is the lack of methods in fusing heterogeneous data from multimodal sensors to accurately perceive the dynamic states of construction entities. The congested and dynamic nature of construction sites has posed great challenges such as signal interference and line of sight occlusion to a single mode of sensor that is bounded by its own limitation in perceiving the site dynamics. The research hypothesis is that combining data of multimodal sensors that serve as mutual complementation achieves improved accuracy in perceiving dynamic states of construction entities. This research proposes a hybrid framework that leverages vision-based localization and radio-based identification for robust 3D tracking of multiple construction workers. It treats vision-based tracking as the main source to obtain object trajectory and radio-based tracking as a supplementary source for reliable identity information. It was found that fusing visual and radio data increases the overall accuracy from 88% and 87% to 95% and 90% in two experiments respectively for 3D tracking of multiple construction workers, and is more robust with the capability to recover the same entity ID after fragmentation compared to using vision-based approach alone.<br></p> <p>The second knowledge gap is the missing link between entity interaction patterns and diverse activities on the jobsite. With multiple construction workers and equipment co-exist and interact on the jobsite to conduct various activities, it is extremely difficult to automatically recognize ongoing activities only considering the spatial relationship between entities using pre-defined rules, as what has been done in most existing studies. The research hypothesis is that incorporating additional features such as attentional cues better represents entity interactions and advanced deep learning techniques automates the learning of the complex interaction patterns underlying diverse activities. This research proposes a two-step long short-term memory (LSTM) approach to integrate the positional and attentional cues to identify working groups and recognize corresponding group activities. A series of positional and attentional cues are modeled to represent the interactions among entities, and the LSTM network is designed to (1) classify whether two entities belong to the same group, and (2) recognize the activities they are involved in. It was found that by leveraging both positional and attentional cues, the accuracy increases from 85% to 95% compared with cases using positional cues alone. Moreover, dividing the group activity recognition task into a two-step cascading process improves the precision and recall rates of specific activities by about 3%-12% compared to simply conducting a one-step activity recognition.<br></p> <p>The third knowledge gap is the non-determining role of jobsite context on entity movements. Worker behavior on a construction site is goal-based and purposeful, motivated and influenced by the jobsite context including their involved activities and the status of other entities. Construction workers constantly adjust their movements in the unstructured and dynamic workspace, making it challenging to reliably predict worker trajectory only considering their previous movement patterns. The research hypothesis is that combining the movement patterns of the target entity with the jobsite context more accurately predicts the trajectory of the entity. This research proposes a context-augmented LSTM method, which incorporates both individual movement and workplace contextual information, for better trajectory prediction. Contextual information regarding movements of neighboring entities, working group information, and potential destination information is concatenated with movements of the target entity and fed into an LSTM network with an encoder-decoder architecture to predict trajectory over multiple time steps. It was found that integrating contextual information with target movement information can result in a smaller final displacement error compared to that obtained only considering the previous movement, especially when the length of prediction is longer than the length of observation. Insights are also provided on the selection of appropriate methods.<br></p><p>The results and findings of this dissertation will augment the holistic situational awareness of site entities in an automatic way and enable them to have a better understanding of the ongoing jobsite context and a more accurate prediction of future states, which in turn allows the proactive detection of any potential collisions.<br></p>

Construction worker safety is normally a construction activity in the United States, even though there is an emerging body of literature discussing the positive effects of considering safety earlier in the construction lifecycle.  This literature has discussed the fragmentation in terms of safety attitudes between owners and designers and those carrying out the construction of a project.  Quantitatively identifying the specific areas that the differences exist in terms of safety attitudes between common roles on a construction project could be a step toward reducing the fragmentation that currently exists in the work system and promoting safety to be more of a consideration earlier in the project lifecycle.  One common technique for measuring safety attitudes is the use of safety climate survey instruments, but in the construction industry these have historically focused on just construction personnel.  This research will discuss the development of a survey instrument to measure differences in safety attitudes between typical members of the entire construction project work system in order to identify specific areas that gaps exist.  Phase I of the research include the development of an instrument using Mohammed\'s (2002) survey as a base, validation of the measurement model using Confirmatory Factor Analysis, and using applied nonparametric statistics to analyze the data and identify significant differences.  These results will be used in Phase II to develop a training tool to educate relevant project personnel on differences that were identified in Phase I, and to determine the best mediums for conveying this type of information. / Ph. D.

The construction industry is one of the most unsafe industries for workers in the United States. Advancements in wearable technology have been proven to create a safer construction environment. Despite the availability of these devices, use within the construction industry remains low. The objective of this research is to identify and analyze the causes behind the reluctance of the construction industry to implement two specific wearable safety devices, a biometric sensor, and a location tracking system. Device acceptance was analyzed from the perspective of the user (construction field labor) and company decision makers (construction managers). A modified unified theory of acceptance and use of technology (UTAUT) model was developed specific to barriers commonly found within technology adoption in the construction industry including: perceived performance expectancy, perceived effort expectancy, openness to data utilization, social influence, data security, and facilitating conditions. A structured questionnaire was designed to test for association between the mentioned constructs and either behavioral intention or actual use. The questionnaire went through an expert review process, and a pilot study was conducted prior to being distributed to industry. Once all data was received Pearson chi-squared analysis was used to test for association between the constructs. A minority (46%) of labor respondents would not agree to voluntarily use the biometric wearable sensing device. Constructs associated with this finding included perceived performance expectancy, perceived effort expectancy, and social influence. A majority (59%) of labor respondents would not agree to voluntarily use the location tracking wearable sensing device. Constructs associated with this finding included perceived performance expectancy, social influence, and data security. A majority (56%) of management respondents would not implement the biometric wearable sensing device. Constructs found to be associated with this finding included perceived performance expectancy, openness to data utilization, and social influence of the client. A supermajority (68%) of management respondents would not implement the location tracking wearable sensing device. Constructs found to be associated with this finding include perceived performance expectancy, perceived effort expectancy, openness to data utilization, social influence, and data security. This study will aid in the successful implementation of wearable sensing devices within the construction industry. Findings from this study can be used to aid those hoping to implement wearable sensing devices by identifying causes of wearable sensing device rejection. The results of this study can be used by both project managers and health and safety professionals to aid in device acceptance by field labor, and by those whose goal is to increase device use among construction firms.

No description available.

Safety is a continually significant issue in the construction industry. The Occupation Safety and Health Administration as well as individual construction companies are constantly working on verifying that their selected safety plans have a positive effect on reduction of workplace injuries. Worker safety is a large concern for both the workers and employers in construction and the government also attempts to impose effective regulations concerning minimum safety requirements. There are many different methods for creating and implementing a safety plan, most notably the Construction Industry Institute's (CII) Zero Accidents Techniques (ZAT). This study will attempt to identify a relationship between the level of ZAT implementation and safety performance on industrial construction projects. This research also proposes that focusing efforts on certain ZAT elements over others will show different safety performance results. There are three findings in this study that can be used to assist safety professionals in designing efficient construction safety plans. The first is a significant log-log relationship that is identified between the DEA efficiency scores and Recordable Incident Rate (RIR). There is also a significant difference in safety performance found between the Light Industrial and Heavy Industrial sectors. Lastly, regression is used to show that the pre-construction and worker selection ZAT components can predict a better safety performance. / Master of Science

Thesis (MEng)--Stellenbosch University, 2014. / One of the causes for money to be wasted on construction sites is accidents. The reason is that an accident on site is an unplanned event typically relating to the loss of production or the loss of life. Many industry stakeholders and role players have focused on construction health and safety and to improve this area of concern; however, construction health and safety are not significantly improving. Construction still continues to contribute a large number of fatalities and injuries relative to other industry sectors. During the construction phase, poor construction health and safety performance is attributable to a lack of management commitment, inadequate supervision, and a lack of health and safety training and - systems. Health and safety systems do not only include excellent health and safety management on site, but rather an integrated approach on health and safety issues from the conceptual design phase by all stakeholders participating. This integrated approach includes the design done by the engineer. The inspiration behind this research is the question of whether South African Engineers design buildings safe for construction. The lack of knowledge by engineers with regard to construction processes, the lack of health and safety enforcement in the engineering offices and construction sites, and whether engineers adhere to safe design principles is the subject of investigation in this research. Therefore, this research aims to investigate the role of the design engineer in the safety of building projects. Specifically, it investigates to what extent the design engineer can contribute to site safety, and to what extent this is actually taking place. The Construction Regulations states the engineer can be appointed to act on behalf of a client and should share any information that might affect the health and safety of construction employees with the contractor. By means of a literature study, the investigation of case studies and the investigation of questionnaires to which a percentage of South African engineers responded, this research identified the information that should be shared by the design engineer with the contractor. The information can be shared by indicating hazardous activities or - locations on the actual drawings. Information can also be shared by specifying and reminding the contractor of certain health and safety hazards in the health and safety specifications of the building project. Although the Construction Regulations state that the safety hazards associated with most construction processes are the responsibility of the contractor, it will be beneficial for the safety of the employees if the engineer also consults the contractor on the hazards identified by him or her during the early design stages. Early collaboration between the engineer and contractor is also beneficial for the safety of construction employees. The result is an integrated approach towards safety hazard identification and mitigation. Having adequate knowledge with regard to construction processes allows the engineer to be aware of possible safety hazards. This will result in the correct information to be shared with the contractor and incorporated into the early design phases of the project to ensure a healthy and safe working environment. The study shows that a percentage of South African engineers have a lack of site experience, a lack of safety training, a lack of knowledge with regard to the content of the Construction Regulations, and a lack of knowledge with regard to construction processes. These shortcomings can be detrimental to site safety.

During the past 10 years, construction was the leading industry of occupational fatalities when compared to other goods producing industries in the US. This is partially attributed to ineffective safety management strategies, specifically lack of automated construction equipment and worker monitoring. Currently, worker safety performance is measured and recorded manually, assessed subjectively, and the resulting performance information is infrequently shared among selected or all project stakeholders. Accurate and emerging remote sensing technology provides critical spatio-temporal data that has the potential to automate and advance the safety monitoring of construction processes. This doctoral research focuses on pro-active safety utilizing radio-frequency location tracking (Ultra Wideband) and real-time three-dimensional (3D) immersive data visualization technologies. The objective of the research is to create a model that can automatically analyze the spatio-temporal data of the main construction resources (personnel, materials, and equipment), and automatically measure, assess, and visualize worker's safety performance. The research scope is limited to human-equipment interaction in a complex construction site layout where proximities among construction resources are omnipresent. In order to advance the understanding of human-equipment proximity issues, extensive data has been collected in various field trials and from projects with multiple scales. Computational algorithms developed in this research process the data to provide spatio-temporal information that is crucial for construction activity monitoring and analysis. Results indicate that worker's safety performance of selected activities can be automatically and objectively measured using the developed model. The major contribution of this research is the creation of a proximity hazards assessment model to automatically analyze spatio-temporal data of construction resources, and measure, evaluate, and visualize their safety performance. This research will significantly contribute to transform safety measures in construction industry, as it can determine and communicate automatically safe and unsafe conditions to various project participants located on the field or remotely.

Construction has a higher injury and fatality rate than most of the other industries. Given this situation, existing research has studied various issues and factors affecting construction safety management and has attempted to use all available methods to improve the construction safety performance. However, the construction accident rate remains among the highest in the United States and the world. The primary objective of this research is to advance autonomous proximity monitoring and hence provide a safer environment for construction workers. In particular, I seek to advance current evaluations of proximity warning technologies to a more robust engineering approach to the design and deployment of autonomous safety monitoring systems. The contributions of the research are demonstrated through specifications, deployments, and testing of proximity monitoring systems for crane loads and falling from height. My research advances current knowledge in three areas. First, I develop specifications for proximity safety monitoring in a sensed environment, built from existing guidelines and expert interviews. Second, I translate the specifications to computer interpretable rules and deploy them in a distributed computing environment. This demonstrates the feasibility of a systems approach and reusability of components to speed deployment. Third, I evaluate the accuracy of the specifications and systems under imperfect data. I further evaluate some approaches to dealing with imperfect data. Collectively, these advances move existing proximity warning research from evaluation of specific systems to an engineering approach to development and deployment of distributed systems with reusable components that explicitly treats imperfect data. / text

Safety of workers in the construction environment remains one of the greatest challenges faced by the construction industry today. Activity-based hazard identification and prevention is limited because construction safety information and knowledge tends to be scattered and fragmented throughout safety regulations, accident records, and experience. With the advancement of information technology in the building and construction industry, a missing link between effective activity-level construction planning and Building Information Modeling (BIM) becomes more evident. The objectives of this study are 1) to formalize the safety management knowledge and to integrate safety aspects into BIM, and 2) to facilitate activity-based hazard identification and prevention in construction planning. To start with, a Construction Safety Ontology is created to organize, store, and re-use construction safety knowledge. Secondly, activity-based workspace visualization and congestion identification methods are investigated to study the hazards caused by the interaction between activities. Computational algorithms are created to process and retrieve activity-based workspace parameters through location tracking data of workers collected by remote sensing technology. Lastly, by introducing workspace parameters into ontology and connecting the ontology with BIM, automated workspace analysis along with job hazard analysis are explored. Results indicate that potential safety hazards can be identified, recorded, analyzed, and prevented in BIM. This study integrates aspects of construction safety into current BIM workflow, which enables performing hazard identification and prevention early in the project planning phase.