There is limited field data that can be used for fuel use and emissions analyses of nonroad diesel construction equipment. This paper summarizes the results of field research that used a portable emission monitoring system (PEMS) to collect fuel use and emissions data from eight backhoes, six bulldozers, three excavators, four generators, six motor graders, three off-road trucks, one skid-steer loader, three track loaders, and five wheel loaders while they performed various duty cycles. These tests produced approximately 119 hours of field data for petroleum diesel and approximately 48 hours for B20 biodiesel. Engine attribute data including horsepower, displacement, model year, engine tier, and engine load were measured to determine their influence on fuel use rates and emission rates of NO x , HC, CO, CO 2 , and opacity. Mass per time fuel use rates were developed for each item of equipment as well as mass per time and mass per fuel used emission rates for each pollutant. For petroleum diesel, fuel use and emission rates of each pollutant were found to increase with engine displacement, horsepower, and load, and to decrease with model year and engine tier. The results were qualitatively similar for B20. Fuelbased emission rates were found to have less variability and less sensitivity to engine size and load than time-based emission rates. Hence, where possible, development of emission inventories based on fuel consumed, rather than time of activity, is preferred.

A study design was developed and demonstrated for deployment of a portable emission measurement system (PEMS) for excavators. Excavators are among the most commonly used vehicles in construction activities. The PEMS measured nitric oxide, carbon monoxide, hydrocarbons, carbon dioxide, and opacity-based particulate matter. Data collection, screening, processing, and analysis protocols were developed to assure data quality and to quantify variability in vehicle fuel consumption and emissions rates. The development of data collection procedures was based on securing the PEMS while avoiding disruption to normal vehicle operations. As a result of quality assurance, approximately 90% of the attempted measurements resulted in valid data. On the basis of field data collected for three excavators, an average of 50% of the total nitric oxide emissions was associated with 29% of the time of operation, during which the average engine speed and manifold absolute pressure were significantly higher than corresponding averages for all data. Mass per time emission rates during non-idle modes (i.e., moving and using bucket) were on average 7 times greater than for the idle mode. Differences in normalized average rates were influenced more by intercycle differences than intervehicle differences. This study demonstrates the importance of accounting for intercycle variability in real-world in-use emissions to develop more accurate emission inventories. The data collection and analysis methodology demonstrated here is recommended for application to more vehicles to better characterize real-world vehicle activity, fuel use, and emissions for nonroad construction equipment.

Spatial databases contain geocoded data. Geocoded data play a major role in numerous engineering applications such as transportation and environmental studies where geospatial information systems (GIS) are used for spatial modeling and analysis as they contain spatial information (e.g., latitude and longitude) about objects. The information that a GIS produces is impacted by the quality of the geocoded data (e.g., coordinates) stored in its database. To make appropriate and reasonable decisions using geocoded data, it is important to understand the sources of uncertainty in geocoding. There are two major sources of uncertainty in geocoding, one related to the database that is used as a reference data set to geocode objects and one related to the interpolation technique used. Factors such as completeness, correctness, consistency, currency, and accuracy of the data in the reference database contribute to the uncertainty of the former whereas the specific logic and assumptions used in an interpolation technique contribute to the latter. The primary purpose of this article is to understand uncertainties associated with interpolation techniques used for geocoding. In doing so, three geocoding algorithms were used and tested and the results were compared with the data collected by the Global Positioning System (GPS). The

Field data for in-use fuel consumption and emission rates were collected for 15 nonroad vehicles using a portable emission measurement system (PEMS). Each vehicle, including 5 backhoes, 4 front end loaders, and 6 motor graders, were tested once on petroleum diesel and once on B20 biodiesel. The vehicles include different model years and thus represent a variety of engine certification tiers. A methodology was developed for study design, field data collection, data screening and quality assurance, data analysis, and benchmarking of the data. The average rate of loss of data due to data quality issues was 6.9 percent. On average, over 3 hours of valid data were collected in each test. Time-based emission factors were found to increase monotonically with respect to engine manifold absolute pressure. Fuel-based emission factors were mainly sensitive to differences between idle and non-idle engine operation. Typical duty cycles were quantified in terms of frequency distributions of manifold absolute pressure (MAP) and used to estimate cycle average emission factors. On average, the use of B20 instead of petroleum diesel lead to an insignificant 1.8 percent decrease in NO emission rate and significant decreases of 18, 26, and 25 percent for opacity, HC, and CO, respectively. Emission rates were also found to decrease significantly when comparing newer, higher tier vehicles to older ones. Fuel use, NO, HC, and CO data were found to be of similar magnitude as independent benchmark data. Specific recommendations are made for future work.

Cost control and schedule control are two of the most important management functions in the construction industry. Major research efforts are focused on developing procedures for improving the effectiveness of cost and schedule control. As a result, researchers are concerned with the quality, integrity, and timeliness of data that flow through such control systems. A number of data models have been proposed to integrate cost-and schedule-control functions, because such integration is viewed as the, solution to the many problems facing construction projects today. This paper provides an overview of cost-and schedule-control functions, defines the desired control cycle, and discusses the problems and needs of cost-and schedule-control functions. A number of integrated cost-and schedulecontrol data models, which represent the state of construction research in this area, are discussed. The work-packaging model is briefly described and is suggested as the most likely existing model to achieve the desired cost and schedule integration. Finally, the conceptual design of a foundational data model for control, based on relational concepts, is provided. The recommended design adopts the conceptual structures of the work-packaging model. 2Res. Asst.,