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.
Simulation models are typically developed for construction operations to maximize output of resources and minimize operational cost. Traditional simulation methods deal with development of activity cycle diagrams based on key activities in construction operations. These methods do not consider spatial constraints at the site directly. As a result, spatial conflicts occur and desired output level is not achieved. Congestion analysis in construction site layout planning depends upon such spatial constraints and movement of resources inside the site. This research implements a cell-based simulation model with spatial consideration for construction site layout planning. The objective of this research is to incorporate congestion analysis into simulation process to improve construction site layout plan. A case study is done involving cyclic earthmoving operation. Location data from moving equipment is collected using Global Positioning System for accurate spatial reference. Real data from site is fed into the simulation model for realistic representation of the operation. A cell-based continuous simulation model is developed for visualizing congestion. A new method for quantifying congestion is proposed. This method will help decision-makers in developing a site layout plan based on movement of resources. The potential for congestion can be determined before implementing a layout onto the site. It will aid in comparing alternative site layout plans and provide insight on consequences of varying the number of resources on site congestion. It will also serve as a training and educational tool for construction managers.
Safety continues to be among the top issues in the construction industry after experiencing 775 fatalities in 2012. Among all those fatalities, falling has been considered as one of the leading contributors for several years. This paper presents a method that automatically identifies fall risks on construction sites under excavation by utilizing laser scanning technology. It first extracts safety rules that are related to fall risks from OHSA standards and construction best practices. Then, it collects sets of point cloud data of a construction site under excavation using laser scanning, registering and cleaning the point cloud data afterwards. Finally, it develops an identification algorithm based on those rules and applies the algorithm onto the data to identify potential fall risks by analyzing geometrical properties. An experimental trial is also conducted in this paper and results show that the method successfully identifies those fall risks. The presented method can actively monitor the fast changing situations of construction sites under excavation and provide inspectors and project managers with valuable information about fall risks, helping them make good safety decisions and prevent fall accidents and fatalities.
Safety continues to be among the top issues in the construction industry after experiencing 738 fatalities in the United States in 2011. Among all construction operations, excavation is one of the most hazardous because of its possible cave-ins, contact with objects and equipment, bad air, and so on. Until today, most safety inspectors still are inspecting the site manually, making the inspection time consuming and error prone. This paper presents a method that automatically identifies cave-in safety risks in construction excavation. It first extracts relevant safety rules from OHSA standards and industrial best practices. Then it collects a set of point cloud data of a construction site under excavation using laser scanning, registering, and cleaning the point cloud data afterward. Finally, it develops an automated identification algorithm on the basis of those rules and applies the algorithm to the data to identify potential cave-in risks by analyzing geometrical properties. An experimental trial also is conducted in this paper, and results show that the method identifies those cave-in risks successfully. The presented method actively monitors the fast changing situations of construction sites under excavation and helps inspectors and project managers make good safety decisions, preventing accidents and fatalities.
Global Navigation Satellite Systems (GNSS) are widely used to document the on- and off-site trajectories of construction equipment. Before analyzing the collected data for better understanding and improving construction operations, the data need to be freed from outliers. Eliminating outliers is challenging. While manually identifying outliers is a time-consuming and error-prone process, automatic filtering is exposed to false positives errors, which can lead to eliminating accurate trajectory segments. This paper addresses this issue by proposing a hybrid filtering method, which integrates experts’ decisions. The decisions are operationalized as parameters to search for next outliers and are based on visualization of sensor readings and the human-generated notes that describe specifics of the construction project. A specialized open-source software prototype was developed and applied by the authors to illustrate the proposed approach. The software was utilized to filter outliers in sensor readings collected during earthmoving and asphalt paving projects that involved five different types of common construction equipment.
A literature review revealed several major shortcomings in the analysis of construction equipment operations data, for example, the lack of using realistic or real-time positioning data that can feed into an equipment operations analysis or simulation model. This paper presents technology and algorithms that have the potential in aiding the automated assessment of construction site equipment operations. Utilizing commercially available low-cost global positioning system (GPS) devices enables the continuous data logging of equipment location in addition to simultaneously recording timestamps. However, before any such spatio-temporal equipment data can be reliably collected on construction sites, the error rate of the GPS devices had to be evaluated. Data analysis methods and rules for monitoring construction site equipment operations and activity were then defined. A detailed software interface was finally created that allows a user to set, analyze, and visualize several important equipment parameters towards achieving the goal of creating more realistic equipment operation analysis and potential for inclusion in simulation models. Results from field experiments show that the developed technology is able to identify and track equipment activity- and safety-related information automatically for job site performance and layout decision making, respectively. The presented work will aid construction project managers in making better decisions to plan, manage, and control equipment-related work tasks on construction sites.
Previous research and applications in construction resource optimization have focused on tracking the location of material and equipment. There is a lack of studies on automated monitoring of the interaction between workers and equipment for safety purposes. This paper presents a new approach for measuring the safety performance of construction personnel particularly when they work in very close proximity to moving equipment as well as static hazards such as chemical and flammable substances. A method of generating hazardous zones according to the geometric and kinematic characteristics of the considered hazard is introduced. The spatio-temporal relationships between the hazardous zones and workers’ positioning data collected by real-time location sensing technology are automatically analyzed. This approach has been validated in a controlled test bed environment that simulates a construction site. Results indicate that worker’s safety performance of selected activities can be automatically and reliably measured using the developed approach. Furthermore, a heat map is generated for visualizing proximity related issues in the test bed using the computed results.
Recording the continuous location of equipment and workers with Global Positioning System (GPS) units can contribute in the analysis of how safely a construction site operates. Automated data gathering and analysis for safety become even more valuable when reliable methods exist that record and report events that otherwise would not be recorded because they are labor intensive in observation or prone to human error in judgment. This paper presents a new safety approach that features automated analysis of continuously collected proximity data between construction workers, heavy construction equipment, and hazardous construction spaces in outdoor construction environment. We recorded field data using small GPS units that were mounted on construction helmets or attached to construction equipment. This paper first evaluates the performance of the technology that was used to gather continuous location data to construction resources (workers and equipment). It then explains how the generated spatio-temporal information can be communicated to decision makers so it improves the safety performance of workers near equipment or other hazards. The results that are presented include a case study to outdoor construction environment. It demonstrates how potential users can measure the safety performance of construction resources (workers, equipment) automatically and use the generated information as new knowledge in safety training and education.
The purposes of this research are to develop and evaluate a framework that utilizes the integration of commercially-available Radio Frequency Identification (RFID) and a BIM model for real-time resource location tracking within an indoor environment. A focus of this paper is to introduce the framework and explain why building models currently lack the integration of sensor data. The need will be explained with potential applications in construction and facility management. Algorithms to process RFID signals and integrate the generated information in BIM will be presented. Furthermore, to demonstrate the benefits of location tracking technology and its integration in BIM, the paper provides a preliminary demonstration on tracking valuable assets inside buildings in real-time. The preliminary results provided the feasibility of integrating passive RFID with BIM for indoor settings.
The hypothesis is that leveraging automated data collection technology for site status analysis would play a more significant role in advancing decision making in construction projects if applied to traditional labor intensive management work tasks. Such manual data record keeping is, for example, progress tracking measurements and reporting of daily work status and process flows. Recent research on material tracking has demonstrated that the implementation of automated material tracking technology is feasible. Studies have yet to demonstrate whether the same or other technology can be used on other resource types, including workers, and furthermore, in advancing technology that works bi-directional: (1) collect and analyze data, and (2) return automated feedback to the decision makers at the management or even the workforce level.
Despite a rigorous cost–benefit, hardware reliability and safety tests, implementation of technology in field operations is often performed on an as-needs basis. Project based case studies are effective research tools to measure the benefits and barriers that technology comes with. This paper will demonstrate results to the design, development, and furthermore and mainly, the effective and affordable implementation of a state-of-the-art wireless passive RFID-based technology system that collects and distributes information from and to decision makers. The developed technology was tested for several consecutive months on more than 50 construction workers, material carts and work related items, and personnel and material lifts that were critical in a high-rise building renovation project. Metrics to measure success in the phases of data collection, the signal and data processing, and in the use of newly generated or already available information for advanced decision making based on passive RFID technology will be presented.
Emerging wireless remote sensing technologies offer significant potential in advancing the management of construction processes by providing real-time access to the locations of
workers, materials, and equipment. Unfortunately, little is known regarding the reliability and practical benefits of emerging sensing technology integrated with Building Information Modelling
(BIM) within an indoor building environment. This limitation effectively impedes widespread adoption of sensing technology. This paper introduces and evaluates the framework that integrates
commercially-available Radio Frequency Identification (RFID) technology for real-time, mobile resource location tracking in a BIM model. The need will be explained with potential applications
in construction and facility management. Algorithms to process RFID signals and integrate the generated information in BIM will be presented. Furthermore, to demonstrate the benefits of
location tracking technology and its integration in BIM, the paper provides a preliminary demonstration on tracking valuable assets inside buildings in real-time.
Design-bid-build (DBB) projects are procured by government agencies typically through the competitive bidding process. The decision of the contracting authorities regarding which projects proceed to the bidding stage depends, in part, upon the early estimates of probable cost. Efforts are made to make this estimate as realistic as possible. Irrespective of the estimate of probable cost, the actual project cost is established by the amount of the winning bid and the cost of change orders during construction phase. The change order costs generally are known during the construction phase of the projects. However, the bid cost of projects can be estimated by analyzing the bid data of historical projects. This study will develop a method to predict the future projects’ bid cost of unit price items based on quantities of items. The regression models for various unit price items will be developed by analyzing historical 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. Statistical models were developed to improve the methodologies for estimating bid-item unit pricing and to reduce variances that result in large discrepancies between engineers’ estimates and actual bid-award amounts. These regression models can be used to predict the actual bid cost of the unit price items based on quantity of the unit items.
This paper presents an automated GPS-based method for assessing construction equipment operations productivity. The literature revealed several shortcomings in simulation of construction equipment, for example, the availability of realistic data that supports a simulation framework, and identified the need for integrating real-time field data into simulations. Commercially available GPS-based data logging technology was then evaluated. Analysis methods and rules for monitoring productivity were also discussed. A software interface was created that allowed to analyze and visualize several important parameters towards creating more realistic simulation models. The experimental results showed a productivity assessment method by collecting spatio-temporal data using GPS data logging technology, applied to construction equipment operations, and finally identified and tracked productivity and safety based information for job site layout decision making. This research aids construction project managers in decision making for planning work tasks, hazard identification, and worker training by providing realistic and real-time project equipment operation information.
The use of information technology (IT) in construction is expanding rapidly. More and more architecture, engineering, and construction companies are adopting new technologies in software design to help accelerate and accurately carry out their functions. Based on a research study carried out in Southern Nevada with a questionnaire survey of 54 construction-related firms, this study aims to compare the types and extent of IT use by architects, engineers, and contractors. It also explores the level of IT skills possessed by professionals working in the industry. The study found that architects mostly used design software whereas engineers and contractors used scheduling and estimating software. All of the respondents believed that IT helped to improve their work productivity. The majority of the respondents thought that IT is useful for Construction Management (CM) students: engineers and architects thought that CM students should take more scheduling and estimating software courses and contractors thought that they should take more scheduling and quantity take-off software courses.
Information Technology (IT) is an important tool for improving business practices. From the early-1980’s, there has been a huge increase in the adoption of IT in every field. Architecture/ Engineer/ Contractor (A/E/C) community has been a heavy adopter and is significantly more dependent on IT than a decade ago in its daily work. Southern Nevada has been one of the world’s most robust construction markets for the past decade. This study collected data from 28 A/E/C community member firms within Southern Nevada. The study identifies areas where IT is most used and the types of IT used. The research findings will benefit academics by identifying what IT is presently in wide-scale use, and providing an indication of what should presently be integrated into their courses and curriculum.
The vast majority of highway and street projects in the United States are procured by state or local governments through a competitive bidding process. In a competitively bid public project, awarding the contract to the lowest bidder is a predominant practice. A few studies measured the effect of awarding the contract to the lowest bidder on construction cost growth. This paper analyzes a sample of 435 bids on 113 public street projects in Clark County, Nevada, to determine a correlation between a lowest bid price and construction cost growth. The sample included projects completed between 1991 and 2008 and more than $554 million in construction value. The study also determined a correlation between the number of bidders and the deviation of the bid cost from the engineers’ estimate. The study showed no correlation between the lowest bid price and the construction cost growth. It showed that public owners would have received the lowest construction bid price if more bidders had been involved in the bidding process. The study found a strong correlation between the lowest bid price and the final construction cost. Therefore, a regression model was developed to predict the final construction cost of a street project by using the lowest bid price. A validation of the model showed that on average the predicted cost of a project was within 3.51% of the actual construction cost. Other findings included recommendations for future study.
The vast majority of highway and street projects in the United States are procured by state or local governments through a competitive bidding process. In a competitively bid public project, awarding the contract to the lowest bidder is a predominant practice. A few studies measured the effect of awarding the contract to the lowest bidder on construction cost growth. This paper analyzes a sample of 435 bids on 113 public street projects in Clark County, Nevada, to determine a correlation between a lowest bid price and construction cost growth. The sample included projects completed between 1991 and 2008 and more than $554 million in construction value. The study also determined a correlation between the number of bidders and the deviation of the bid cost from the engineers’ estimate. The study showed no correlation between the lowest bid price and the construction cost growth. It showed that public owners would have received the lowest construction bid price if more bidders had been involved in the bidding process. The study found a strong correlation between the lowest bid price and the final construction cost. Therefore, a regression model was developed to predict the final construction cost of a street project by using the lowest bid price. A validation of the model showed that on average the predicted cost of a project was within 3.51% of the actual construction cost. Other findings included recommendations for future study.