Assessing the Impact of the Lead/Lag Times on the Project Duration Estimates in Highway Construction

The literature mentions multiple factors that can affect the accuracy of estimating the project duration in highway construction, such as weather, location, and soil conditions. However, there are other factors that have not been explored, yet they can have significant impact on the accuracy of the project time estimate. Recently, TxDOT raised a concern regarding the importance of the proper estimating of the lead/lag times in project schedules. These lead/lag times are often determined based on the engineer’s experience. However, inaccurate estimates of the lead/lag time can result in unrealistic project durations. In order to investigate this claim, the study utilizes four time sensitivity measures (TSM), namely the Criticality Index (CI), Significance Index (SI), Cruciality Index (CRI), and the Schedule Sensitivity Index (SSI) to statistically analyze and draw conclusions regarding the impact of the lead/lag time estimates on the total duration in highway projects. An Excel-based scheduling software was developed with Monte Carlo simulation capabilities to calculate these TSM. The results from this paper show that the variability of some lead/lag times can significantly impact the accuracy of the estimated total project duration. It was concluded that the current practices used for estimating the lead/lag times are insufficient. As such, it is recommended to utilize more robust methods, such as the time sensitivity measures, to accurately estimate the lead/lad times in the projects scheduled

Moral Hazard, Incentive Contracts and Risk: Evidence from Procurement

Deadlines and penalties are widely used to incentivize effort. We model how these incentive contracts affect the work rate and time taken in a procurement setting, characterizing the efficient contract design. Using new micro-level data on Minnesota highway construction contracts that includes day-by-day information on work plans, hours actually worked and delays, we find evidence of moral hazard. As an application, we build an econometric model that endogenizes the work rate, and simulate how different incentive structures affect outcomes and the variance of contractor payments. Accounting for the traffic delays caused by construction, switching to a more efficient design would substantially increase welfare without substantially increasing the risk borne by contractors.

Weather-related Construction Delays in a Changing Climate: A Systematic State-of-the-art Review

Adverse weather delays forty-five percent of construction projects worldwide, costing project owners and contractors billions of dollars in additional expenses and lost revenue each year. Additionally, changes in climate are expected to increase the frequency and intensity of weather conditions that cause these construction delays. Researchers have investigated the effect of weather on several aspects of construction. Still, no previous study comprehensively (1) identifies and quantifies the risks weather imposes on construction projects, (2) categorizes modeling and simulation approaches developed, and (3) summarizes mitigation strategies and adaptation techniques to provide best management practices for the construction industry. This paper accomplishes these goals through a systematic state-of-the-art review of 3207 articles published between 1972 and October 2020. This review identified extreme temperatures, precipitation, and high winds as the most impactful weather conditions on construction. Despite the prevalence of climate-focused delay studies, existing research fails to account for future climate in the modeling and identification of delay mitigation strategies. Accordingly, planners and project managers can use this research to identify weather-vulnerable activities, account for changing climate in projects, and build administrative or organizational capacity to assist in mitigating weather delays in construction. The cumulative contribution of this review will enable sustainable construction scheduling that is robust to a changing climate

Automated Productivity Models for Earthmoving Operations

Earthmoving operations have significant importance, particularly for civil infrastructure projects. The performance of these operations should be monitored regularly to support timely recognition of undesirable productivity variances. Although productivity assessment occupies high importance in earthmoving operations, it does not provide sufficient information to assist project managers in taking the necessary actions in a timely manner. Assessment only is not capable of identifying problems encountered in these operations and their causes. Many studies recognized conditions and related factors that influence productivity of earthmoving operations. These conditions are mainly project-specific and vary from one project to another. Most of reported work in the literature focused on assessment rather than analysis of productivity. This study presents three integrated models that automate productivity measurement and analysis processes with capabilities to detect different adverse conditions that influence the productivity of earthmoving operations. The models exploit innovations in wireless and remote sensing technologies to provide project managers, contractors, and decision makers with a near-real-time automated productivity measurement and analysis. The developed models account for various uncertainties associated with earthmoving projects. The first model introduces a fuzzy-based standardization for customizing the configuration of onsite data acquisition systems for earthmoving operations. While the second model consists of two interrelated modules. The first is a customized automated data acquisition module, where a variety of sensors, smart boards, and microcontrollers are used to automate the data acquisition process. This module encompasses onsite fixed unit and a set of portable units attached to each truck used in the earthmoving fleet. The fixed unit is a communication gateway (Meshlium®), which has integrated MySQL database with data processing capabilities. Each mobile unit consists of a microcontroller equipped with a smart board that hosts a GPS module as well as a number of sensors such as accelerometer, temperature and humidity sensors, load cell and automated weather station. The second is a productivity measurement and analysis module, which processes and analyzes the data collected automatically in the first module. It automates the analysis process using data mining and machine learning techniques; providing a near-real-time web-based visualized representation of measurement and analysis outcomes. Artificial Neural Network (ANN) was used to model productivity losses due to the existence of different influencing conditions. Laboratory and field work was conducted in the development and validation processes of the developed models. The work encompassed field and scaled laboratory experiments. The laboratory experiments were conducted in an open to sky terrace to allow for a reliable access to GPS satellites. Also, to make a direct connection between the data communication gateway (Meshlium®), initially installed on a PC computer to observe the received data latency. The laboratory experiments unitized 1:24 scaled loader and dumping truck to simulate loading, hauling and dumping operations. The truck was instrumented with the microcontroller equipped with an accelerometer, GPS module, load cell, and soil water content sensor. Thirty simulated earthmoving cycles were conducted using the scaled equipment. The collected data was recorded in a micro secure digital (SD) card in a comma separated value (CSV) format. The field work was carried out in the city of Saint-Laurent, Montreal, Quebec, Canada using a passenger vehicle to mimic the hauling truck operational modes. Fifteen Field simulated earthmoving cycles were performed. In this work two roads with different surface conditions, but of equal length (1150 m) represented the haul and return roads. These two roads were selected to validate the developed road condition analysis algorithm and to study the model’s capability in determining the consequences of adverse road conditions on the haul and return durations and thus on the tuck and fleet productivity. The data collected from the lab experiments and field work was used as input for the developed model. The developed model has shown perfect recognition of the state of truck throughout the fifteen field simulated earthmoving cycles. The developed road condition analysis algorithm has demonstrated an accuracy of 83.3% and 82.6% in recognizing road bumps and potholes, respectively. Also, the results indicated tiny variances in measuring the durations compared with actual durations using time laps displayed on a smart cell telephone; with an average invalidity percentage AIP% of 1.89 % and 1.33% for the joint hauling and return duration and total cycle duration, respectively

Development of a Wireless Real-Time Productivity Measurement System for Rapid Bridge Replacement

Increased attention has been paid to rapid bridge replacement, one of the critical components of the nation’s transportation network, since the terrorist attacks on September 11, 2001. To enhance the capability of rapid replacement of damaged bridges after extreme events, a prototype wireless real-time productivity measurement system has been developed. The developed system has a potential not only to improve the accuracy of construction schedule but also to strengthen the communication and coordination among parties involved in the replacement process after extreme events by providing accurate productivity information in real time. To validate the developed system, field experiments were conducted at three construction sites. Results of data analyses indicate that it is feasible to use the developed system to measure on-site productivity in real time; and productivity measurements were accurate and could be shared among all parties involved in the replacement process

Climate change and disaster impact reduction

Based on papers presented at the 'UK - South Asia Young Scientists and Practitioners Seminar on Climate Change and Disaster Impact Reduction' held at Kathmandu, Nepal on 5-6 June, 2008

Evaluation of the Most Significant Factors Influencing the Production Rates of Highway Construction Activities

The utilization of realistic production rates is key for the accurate estimation of the contract time in highway projects. Several factors have an impact on production rates and change the total duration of construction projects. To organize and complete projects in a timely, quality and financially responsible manner, projects need to be scheduled carefully. Schedulers and planners of the Texas Department of Transportation (TxDOT) have been noticing that the estimated timelines are far from the reality and want to investigate if external factors can be the cause of this discrepancy. Some of the factors considered in this study are the systems used to schedule highway projects, weather conditions, temperature, location and shifts. This paper aims to investigate how these factors affect projects’ schedules in order to help schedulers create more realistic timelines. Past highway projects developed in Texas, were used to extract the information valuable to perform statistical analysis and determine if these factors have a significant effect on the productivity rates of construction activities

Highway Construction Productivity Measurement with a Wireless Real-Time Productivity Measurement System

Seonghoon Kim, Yong Bai, Yang-Ki Jung, and Dukgeun Yun, Highway Construction Productivity Measurement with a Wireless Real-Time Productivity Measurement System, Transportation Research Record: Journal of the Transportation Research Board (Volume: 2228 issue: 1) pp. 26-33. Copyright © 2011 SAGE Publications. DOI: 10.3141/2228-04.Improving the quality of construction schedules calls for development of an advanced productivity measurement system. Existing on-site construction productivity measurement methods have some common limitations, such as not providing data necessary for engineers and project managers to conduct real-time analyses and share data with other project participants. A wireless real-time productivity measurement (WRITE) system was developed to address those shortfalls. The field experiment was conducted at two different stages: asphalt paving projects, including hot-mix asphalt and hot-in-place recycling, and a bridge reconstruction project. Productivity data collected from the WRITE system were also compared with productivity data collected from construction documents, such as contractors' daily logs and pay estimate documents, to identify the feasibility of this system for measuring the performance of construction projects. For data analyses, statistical methods such as normality test, paired t-test, and Wilcoxon signed-rank test were used. The result of statistical analyses proved that the developed system generated identical productivity measurements compared with the stopwatch method and construction documents. The success of this research project made several major contributions to the advancement of the construction industry. First, the research advanced the application of wireless technology in highway construction operations. Second, it provided an advanced technology for engineers and project managers to determine productivity in real time. Third, productivity data can be shared between project participants via the Internet. With these advancements, communication and coordination will be improved at construction sites. Consequently, the WRITE system will enhance owners' and contractors' ability to manage construction projects

Considering Vermont\u27s Future in a Changing Climate: The First Vermont Climate Assessment

The Vermont Climate Assessment (VCA) paints a vivid picture of a changing climate in Vermont and calls for immediate strategic planning to sustain the social, economic and environmental fabric of our state. The VCA is the first state-scale climate assessment in the country and speaks directly to the impacts of climate change as they pertain to our rural towns, cities and communities, including impacts on Vermont tourism and recreation, agriculture, natural resources and energy