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.

Despite many studies on infrastructure resilience in the existing literature, there is a limited empirical understanding of disaster resilience in the context of intermittent infrastructure systems. To fill this knowledge gap, our study provides an example assessment of the resilience of Kathmandu Valley’s electricity and water supply infrastructure systems in the 2015 Gorkha Earthquake. The study is based on qualitative data collected over a period of one year following the earthquake, obtained through in-depth interviews (n=52), a focus group, and a review of secondary sources. A resilience assessment framework that includes eight factors adapted from existing studies: vulnerability, anticipation, redundancy, adaptive capacity, rapidity, resourcefulness, cross-scale interactions, and learning culture, was used for the data analysis. The characteristics of intermittent infrastructure systems pertaining to resilience identified in this study could have important implications for engineers and decision-makers in developing communities to better design and maintain infrastructure in the face of disasters.

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.

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.

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.

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.

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.

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.

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.