In recent years, several developments have been made towards improving construction methodology and equipment used so as to facilitate better construction environment and create safer working sites. Yet there always remain repetitive construction tasks demanding heavy physical performance from the workers, seemingly safe at the time of performance but eventually causing musculoskeletal disorders. This research explores an approach to monitor the postural behavior of a subject during a lifting operation and provide personalized feedback to the subject to encourage ergonomically safe lifting technique. Posture was measured by tracking subjects with a Kinect camera. The joints were extracted from the skeleton and ergonomic analysis was performed on the extracted lift data. OSHA offers a guideline for safe lifts but does not provide a quantitative technique of analysis. This method also puts effort to device a way to quantify such guidelines for a given scenario. The main and final goal of this study is to leverage virtual reality as a personalized learning environment in which the subjects can interactively learn about safety from their own data as well as their peers.
Maximizing safety often means operating a construction site at its highest sustainable level of safety. This sustainable level of safety is of major interest for safety managers because knowledge of such level can help them identify areas and opportunities of enhancing safety on the jobsites. Safety strategies and plans are made by the managers based on their perception of such sustainable safety. No formalized method exists to determine such level of safety for a construction site. OSHA regulations provide a general guideline but do not consider specific site conditions. The regulations also do not provide insight on what can be done beyond the mandatory requirements to maximize the level of safety and what level of safety can be attained and sustained on a site. To address this problem, this paper proposes a novel framework to identify the sustainable level of safety for a given condition at site. The method builds upon a two-way approach in which a theoretical maximum level of safety and observed level of safety govern the sustainable safety at site. The method also intends to explore the inefficiencies at the jobsite and help identify the areas of improvement. The scope of the paper is limited to labor-intensive lifting operation and relies on skeletal data collected by Kinect camera for illustration purposes. The paper outlines the method and the components of the framework and provides an illustration through a lab-based experiment. The method can potentially help the safety managers to improve their strategies based on real data collected from the actual site and set realistic goals for safety management on construction sites. The method can also be implemented to automatically analyze safety and make recommendations based on real-time data collected from the site.
The cycle time of construction equipment for earthwork operations has a significant impact on project productivity. Elements that directly impact a haul vehicle’s cycle time must be identified in order to accurately quantify the haul cycle time and implement strategies to decrease it. The objective of this research is to scientifically identify and quantify variables that have a significant impact on the cycle time of a dump truck used for earthwork. Real-time location data collected by GPS devices deployed in an active earthwork moving construction site was analyzed using statistical regression. External data including environmental components and haul road conditions were also collected periodically throughout the study duration. Several statistical analyses including a variance analysis and regression analysis were completed on the dump truck location data. Collected data was categorized by stage of the dump truck cycle. Results indicate that a dump truck’s enter idle time, exit idle time, moving speed and driver visibility can significantly impact the dump truck cycle time. The contribution of this research is the identification and analysis of statistically significant correlations of variables within the cycle time.
Safety management is paramount because it does not only pertain to workers’ safety but also impacts productivity, profitability, and employees’ morale directly. Every construction project is unique and safety management strategies developed for one project does not necessarily cater to the needs of another project. To achieve a project-based safety management strategy, this research introduces a novel concept of Safety Frontier – the theoretical maximum level of safety that can be achieved in perfect conditions under good management and typical field conditions. The objective of this research is to identify and isolate the factors affecting worker’s safety while performing labor-intensive operations and propose a method to determine the safety frontier empirically. A Kinect camera was used to record, a sample dataset which was manually annotated to analyze sub-operations. This research only focuses on body posture as a long-term safety issue but it reports the challenges while determining the thresholds for safety and effect of other factors on the overall safety situation. The determination of the highest level of safety can act as a yardstick benchmark to evaluate effectiveness of safety management strategies. It can assist safety managers to formulate adaptive safety management strategies and help them understand the training and supervision needs at construction sites.
Virtual Reality (VR) is widely used in conception, planning and design phases of a project, mainly for communication and collaboration. During the construction phase, safety demands major attention at any active construction site. Iterating among alternative safety plans in real world conditions can be dangerous, expensive, resource intensive and often not feasible. Safety is not only governed by what regulations are at place; the perception of the workers towards site safety conditions is also equally important. In other words, in addition to imposing safety rules, it is also equally important that the workers feel safe in their working environment. This paper explores the potential of leveraging VR to investigate on perceptual response of workers on site safety conditions. A VR environment is created based on real-time geometric (laser scan) and location (GPS) data collected from moving equipment and workers at an active construction site. Hazards are introduced into the VR scenes in a systematic and controlled manner. Subjects are exposed to the scenes through a head-mounted VR system. The experience of the subjects in different hazardous scenarios are recorded through Affective Sensing devices and a questionnaire. Affective sensing technology can track human physiological responses in real-time. The goal of this research is to leverage VR environment to (i) test the feasibility of using affective sensing devices for tracking human responses to hazardous situations, (ii) study the potential of exposing construction workers to virtual hazards for training purposes. The results from this study will give a better understanding of potential of VR in construction safety training and management.
This paper reports findings from a pilot summer camp program organized to promote construction-related career, especially construction technology related career, in high school students. The camp introduced concepts from Programming, Virtual Reality, and Construction to students who had little to no experience in these fields. The two-week course followed a Project Based Learning curriculum, which was chosen to increase relevant and deep learning in addition to motivation in the students to learn the concepts. The project that spearheaded the program required the students to design and construct a building in a virtual environment using a programming language. To construct this building, the students had to learn to program, be able to use virtual reality environment proficiently and implement concepts from outside the class curriculum of the course, be able to use spatial visualization to design their structure, and collaborate with each other in order to achieve success. The objective of the program was to entice tech-savvy new generation towards construction, which will eventually expedite technological adaptations in the construction industry. The challenges, findings and recommendations from the program are discussed in this paper.
The construction industry measures safety performance through lagging indicators such as counting numbers of illnesses, injuries, and fatalities. Active leading indicators, for example capturing hazardous proximity situations between workers-on-foot and heavy construction equipment, provide an additional metric for construction site personnel safety performance without incurring accidents. This article presents a method for recording, identifying, and analyzing interactive hazardous near miss situations between workers-on-foot and heavy construction equipment. Spatiotemporal GPS data are analyzed to automatically measure a hazard index that is visualized in form of a heat map. The graphical representation of computationally identified individual values in up-to-date building information models allows automatically generated personalized safety performance reports. These are based on specific near miss locations, environmental conditions, and equipment types. The presented research is based on previous isolated research efforts in equipment blind spot measurement, real-time location tracking, and proximity alert technology. It contributes the definitions and experimental validation of new safety parameters – such as entry of worker-on-foot in equipment blind spot – to determine the root causes that lead to equipment- and visibility-related fatalities on construction sites. Analysis of these root causes is important in preventing accidents in the first place.
The construction industry continues to be among the leading industries for workplace fatalities in the United States. After experiencing 824 fatal injuries in 2013, the construction industry ranks as one of the most dangerous work environments when compared with other private industrial sectors in the United States. Conditions of construction sites often produce hazardous proximity situations by requiring pedestrian workers and heavy equipment to operate at close proximity. Injury and fatality statistics indicate that current safety practices of construction workers have proven inadequate. The research aims to design hazard zone around pieces of heavy construction equipment in which site personnel should not enter during construction operations. The scope is limited to construction sites and equipment at a horizontal grade and hazards between heavy construction excavation equipment and workers-on-foot. A framework for creating the hazard zone around a piece of construction equipment is presented including detailed methodology discussions for each step. A user interface is also presented that automatically creates a hazard zone around select pieces of construction equipment based on user-defined parameters. The hazard zone for a dump truck, excavator, and backhoe are shown using the created framework. Results indicate that hazard zones for pedestrian workers can be created around construction equipment to increase hazard awareness for workers. Contributions for this research include a user-friendly hazard zone creation tool and database for safety managers and scientific evaluation data of the created hazard zone framework. Safety standards can be formulated based on the design and implement hazard zones on equipment.
A construction site is a harsh environment demanding entire human senses and attention, even for regular scheduled tasks. Many accidents occur on construction sites because of inability of workers to identify hazards and make timely decisions. To understand why some hazards go unseen, it is crucial to study how workers perceive the site. The objective of this research is to leverage eyetracking technology to study workers’ gazing pattern in a construction environment. A real picture from an active construction site is modified to introduce hazards and a desktop experiment is conducted, in which, subjects are asked to identify the hazards. A different group of subjects are made to make similar observations on a 2D sketchrepresentation of the same construction scenario. Eyetracking data gathered from their observations is analyzed to understand when, how, and which hazards do they recognize and the pattern of recognition is studied. The results of this study will enhance our understanding on the visual factors that govern attention and help workers recognize potential hazards in a construction site. The comparison between the observation pattern in real and sketch-representation is done to assess how subjects respond to artificial images compared to real images. This comparison will test the feasibility of using virtual reality for safety training and simulations.
Despite the emerging literature on resilient infrastructure systems, the number of studies related to developing communities is rather limited. The majority of the existing studies focus mainly on resilience of infrastructure networks in developed countries. Infrastructure networks in developed countries are less vulnerable to the impacts of catastrophic disasters due to the existence of established design codes and management processes and the availability of financial and technological resources. Catastrophic disasters usually have more extensive impacts on infrastructure systems in developing countries. The objective of this study is to investigate the resilience of infrastructure in developing countries using a case study of water system in Kathmandu Valley in the aftermath of the 2015 Nepalese Earthquake. First, a new systemic framework for assessment of infrastructure resilience was developed. Second, data obtained from various sources including pre-disaster condition, post-disaster damage assessments, and interviews with different stakeholders were used in assessment of different components of resilience in the water system.The study investigated three dimensions of resilience in Kathmandu Valley’s water system : (1) exposure ; (2) sensitivity ; and (3) adaptive capacity. Through a systemic analysis, various resilience characteristics such as coupling, response behaviors, and types of interdependencies that affect the resilience of the system were identified. The findings of the study highlight different factors that influenced the resilience of the water system in Kathmandu Valley. These results provide new insights regarding infrastructure resilience in the context of developing countries.
Safety as well as productivity performance in construction is often poor due to congested site conditions. We lack a formalized approach in effective activity-level construction planning to avoid workspace congestion. The purpose of this research is to investigate and prototype a new Building Information Modeling (BIM) enabled approach for activity-level construction site planning that can pro-actively improve construction safety. The presented method establishes automated workspace visualization in BIM, using remote sensing and workspace modeling technologies as an integral part of construction safety planning. Global Positioning System (GPS) data loggers were attached to the hardhats of a work crew constructing cast-in-place concrete columns. Novel algorithms were developed for extracting activity-specific workspace parameters from the recorded workforce location tracking data. Workspaces were finally visualized on a BIM platform for detecting potential workspace conflicts among the other competing work crews or between material lifting equipment. The developed method can support project stakeholders, such as engineers, planners, construction managers, foremen and site supervisors and workers with the identification and visualization of the required or potentially congested workspaces. Therefore, it improves the foundation on how decisions are made related to construction site safety as well as its potential impact on a productive and unobstructed work environment.
Learning curve analysis has been used for decades in construction industry to study the effect of experience on productivity, especially in repetitive jobs. Accurate and reliable estimate of time and cost are the benefits of performing a learning curve analysis. While learning curves exist in all levels of a project, detailed analysis of workers’ learning curve for short-term activities (that only span for a couple of minutes) require meticulous manual observation. This demand in manual effort overshadows the economic benefits from such analyses. Also, manual observations are time consuming, subjective and prone to human errors. This research outlines how data for automating such learning curve analyses can be gathered from a construction site. For this purpose, real-time location data using Global Positioning System (GPS) technology is collected from the workers as they perform their regular activities. Data acquired from GPS technology is used with occupancy grid analysis to calculate the amount of time spent by workers in specific area, which is used to demonstrate the spatio-temporal analysis of learning curve. A linear construction activity is presented as a case study. Results include automatic generation and visualization of learning curves for workers. The proposed method enables minute analysis of learning curves in activity level which can be directly associated to project level by following work breakdown structure. The method can be used in construction field for improving estimation, scheduling and training by project managers.
The construction industry continues to be among the leading industries for workplace fatalities in the U.S. After experiencing 824 fatal injuries in 2013, the construction industry continues to rank as one of the most dangerous work environments when compared to other private industrial sectors in the U.S. Conditions of construction sites often produce hazardous proximity situations by requiring ground workers and heavy equipment to operator at close proximity. The gathered injury and fatality statistics indicate that current safety practices of construction workers have proven inadequate. The objective is to design hazard zone around pieces of heavy construction equipment in which ground personnel should not enter during construction operations. The scope is limited to construction sites and equipment at a horizontal grade and hazards between heavy construction equipment and workers-on-foot. A framework for creating the hazard zone around any piece of construction equipment is presented including detailed methodology discussions for each step. The hazard zone for a dump truck, excavator, and backhoe are shown using the created framework. Construction resource tracking data was used to validate the created hazard zone around a dump truck. Results indicate that hazard zones for ground workers can be created around construction equipment to increase hazard awareness for workers. Furthermore, additional safety standards can be formulated based on the ability to design and eventually implement hazard zones on construction equipment.
Safety as well as productivity performance in construction is often poor due to congested site conditions. We lack a formalized approach in effective activity-level construction planning to avoid workspace congestion. The objective of this study is to investigate and prototype a new Building Information Modeling (BIM) enabled approach for activity-level construction planning that can proactively improve construction safety. The presented method establishes automated workspace visualization in a building information model, using workspace modeling as an integral part of construction safety planning. Algorithms were developed for extracting activity-specific workspace parameters from workforce location tracking data. Global Positioning System (GPS) data loggers were attached to the workers’ hardhats during the stripping activities of formwork of columns. Workspaces were then visualized on a BIM platform. The developed method can support project stakeholders, such as engineers, planners, construction managers, and site workers with the identification and visualization of required and congested workspaces, hence improving the foundation on how decisions are made related to construction site safety and health, as well as its potential impact on a productive, unobstructed work environment.
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.
The construction industry measures worker safety performance through lagging indicators such as counting numbers of illnesses, injuries, and fatalities. Active leading indicators, such as
capturing hazardous proximity situations between ground workers and heavy construction equipment, provide an additional metric for construction site personnel safety performance without incurring worker accidents. This article presents an algorithm for recording, identifying, and analyzing interactive hazardous proximity situations between ground workers and heavy construction equipment. Spatio-temporal GPS data of ground worker and heavy equipment movements are analyzed to automatically measure the frequency and duration of identified hazardous proximity situations. Individual periodic ground worker and equipment operator safety performance with regards to exposure to hazardous proximity situations is reported in detail. The results are integrated with previous research on blind spots and other safety deficiencies of the equipment. By measuring and analyzing leading indicator data of ground workers and heavy equipment, safety managers can identify hazardous situations that may otherwise lead to incidents. Knowledge generated about hazardous proximity issues are disseminated to construction personnel through enhanced safety training and education. Mitigation measures can also be taken for safer construction equipment operation.
Locating a specific utility or finding oneself in an unfamiliar facility can be difficult and time consuming. The ability to visualize the location in real time can reduce the time for manual searching and locating. The proposed research is to utilize commercially available radio frequency identification (RFID) technology for real-time visualization and location tracking in a BIM model. The scope is passive RFID tracking technology and Building Information Modeling (BIM). A novel approach is presented that utilizes current localization techniques and algorithms. A prototype application has been developed that connects the RFID readers with a BIM model and database. Preliminary results demonstrate the feasibility of locating a user inside buildings in actual time. Significantly, the visualization will enable users to pinpoint themselves and a utility in a model, saving time and money.
Simulation is a proven technique for effective construction site layout planning and resource optimization. Historical data is used as input for task durations in traditional simulation approaches. These data are fed into activity cycle diagrams that do not consider spatial constraints. Cell-based simulation with real-time location data can be implemented for more realistic modeling and incorporating spatial changes on the site during project execution. Despite the potential, very little research has been done. The objective of this research is to develop a framework of utilizing real-time data for spatial simulation of cyclic activities on a construction site. Continuous data were collected using a global positioning system, and a cell-based simulation model was developed for spatial consideration of earthmoving cycles. The potential of analyzing and visualizing the effects of varying resource combinations on productivity and traffic congestion onsite were explored. The approach will aid in increasing insight and awareness for decision making in resource management, site layout, and internal traffic control plan. It also will serve as an education and training tool for project managers.
In the United States, the majority of public road projects are constructed using the Design-Bid-Build (DBB) method. DBB projects are procured by government agencies typically through the competitive bidding process. In DBB projects, the early estimates of probable cost of road projects is one of the major factors in making decision regarding which projects proceed to the bidding stage. The final cost of the project will be fixed based on the bid amount of the contractor. If the cost of the project can be estimated based on the bid cost from the historical data, the estimated amount will be more accurate. This study attempted to determine the bid cost of projects by analyzing the bid data of 151 DBB road projects undertaken by the Clark County Department of Public Works in southern Nevada from 1991 through 2008. The total value of construction was equivalent to $841 million when converted into a June 2011 base cost. This study developed regression models to predict a future project’s bid cost of unit price items, based on the quantities of items. The validation of models also showed that these models predicted the unit bid cost accurately. These models will assist in assessing the effect of quantity in accurately estimating the cost of the unit price items and reduce variances that result in large discrepancies between engineers’ estimates and actual bid-award amounts.
The construction industry has consistently suffered the highest number of fatalities among all human involved industries over the years. Safety managers struggle to prevent injuries and fatalities by monitoring at-risk behavior exhibited by workers and equipment operators. Current methods of identifying and reporting potential hazards on site involve periodic manual inspection, which depends upon personal judgment, is prone to human error, and consumes enormous time and resources. This research presents a framework for automatic identification and analysis of potential hazards by analyzing spatio-temporal data from construction resources. The scope of the research is limited to human-equipment interactions in outdoor construction sites involving ground workers and heavy equipment. A grid-based mapping technique is developed to quantify and visualize potentially hazardous regions caused by resource interactions on a construction site. The framework is also implemented to identify resources that are exposed to potential risk based on their interaction with other resources. Cases of proximity and blind spots are considered in order to create a weight-based scoring approach for mapping hazards on site. The framework is extended to perform “what-if” safety analysis for operation planning by iterating through multiple resource configurations. The feasibility of using both real and simulated data is explored. A sophisticated data management and operation analysis platform and a cell-based simulation engine are developed to support the process. This framework can be utilized to improve on-site safety awareness, revise construction site layout plans, and evaluate the need for warning or training workers and equipment operators. It can also be used as an education and training tool to assist safety managers in making better, more effective, and safer decisions.