Keywords: construction automation, robotic applications, drywalls.

Scrapers are useful earthmoving machines as they are independently capable of excavating, hauling, and placing soil. Although neither as effective as excavators (e.g., hoes and shovels) in excavating nor as efficient as trucks in hauling and placing soil, the fact that this one machine performs all three tasks makes it the equipment of choice when large quantities of soil need hauling for distances up to approximately 3000 feet.

To estimate time and cost of scrapers for an earthmoving operation, one considers the soil properties, conditions of haul road and the performance characteristics of the scraper. Commonly, the haul road is divided into segments based on variations in the road grade resistance. The scraper's maximum travel speed is determined for each segment from the equipment performance chart. Determining the most economical scraper among several available models for varying hauling conditions could be a rather tedious and exhausting process.

The purpose of this paper is to present an automated procedure to facilitate the selection of the most economical choice among scrapers available for a specific project. The proposed system provides visible comparisons of all available equipment and allows the user to specify additional resources/costs that might be necessary for a certain project.

A literature review suggests that attempts have been made to develop computer-aided tools to assist in the selection of construction equipment (e.g., trenchers, backhoes, etc). Researchers have developed advisory expert systems targeting specific types of equipment. For example, Touran [5] developed an expert system to aid in the selection of compaction machines. It takes into account the scope, type of soil, degree of compaction required, and soil properties and recommends the best compactor. Hanna [3] created a similar system for crane selection that uses the expected heaviest lift, required maneuverability, and other job conditions to determine the type and size of a suitable crane. Amirkhanian and Baker [1] developed an expert system in which the user is asked a series of questions to define the project conditions and recommend the type and number of pieces of equipment needed. Christian and Xie [2] developed an expert system that takes in consideration the type and quantity of the excavated material and recommends the type of equipment that would best accomplish the work. Kuprenas and Hankhaus [4] developed a system to select the proper scraper for a given set of conditions. However, none of the above systems provided visible comparisons of all available equipment nor allow the user to specify additional resources/costs that might be necessary for a certain project.

The computer-aided procedure presented in this paper is designed specifically for utilization of scrapers. It determines the most economical selection (specific model and optimum number of units) for the job on hand. The procedure can make the selection based on the minimum unit cost ($/cy) or maximum production rate (cy/hour), as required by the user. After entering the project data (e.g., excavation quantities, soil type, haul road conditions, travel distance, etc), the system determines the production rate, number of scrapers needed for the operation, the time needed to complete the operation, and the unit cost for each scraper in the database. The results are tabulated and ranked in the order of the most to the least economical for the user's review. The user can also limit the calculations to a certain scraper selected from the list of scrapers available in the database and view the cost and production rate for that particular scraper. The user has the ability to specify additional resources/costs that need to be considered in determining the unit costs.

The procedure was developed as a spreadsheet application composed of seven integrated worksheets written in Microsoft ExcelRand two subroutines written in Visual Basic for Applications (VBA). The Excel application was structured so that each worksheet serves a certain function (e.g., user interface, equipment database, soils lookup tables, etc). The first subroutine contains the calculations necessary to determine the most economical selection. The second subroutine facilitates the addition of scrapers to the database.

The presented computer-aided procedure should improve the effectiveness of field engineers and estimator as it facilitates data entries, eliminates the time necessary for calculating total resistance, travel speeds, travel time, and accurately determines the operation's cost and performs what if scenarios to identify minimum cost. This system provides a quick and accurate means for performing some mundane calculations that are necessary to determine the most economical selection in terms of type and number of scrapers for a certain job. It allows the user to compare production and cost of different scrapers and allows the user to examine the what-if-scenarios whenever needed. The system could be extended to allow the user to enter limitations on the time allowed for the excavation and request the system to provide the total number of scrapers and pushers required to complete the work within the time allotted.

1

Amirkhanian, S. and Baker, N., "Expert system for equipment selection for earth-moving operation", J. of Construction Eng. and Management - ASCE >, 118, 318-331, 1992. doi:10.1061/(ASCE)0733-9364(1992)118:2(318)

2

Christian, J. and Xie, T.X., "Improving earthmoving estimating by more realistic knowledge", Canadian J. of Civil Engineering, 23, 250-259, 1996.

3

Hanna, A., "SELECTCRANE: An expert system for optimum crane selection", Proceedings on the 1st conference of computing in Civil Engineering, 1, 958-963, 1994.

4

Kuprenas, J. and Henkhaus, T., "SSPE - A tool for scraper selection and production, Computing in civil and building engineering", proceedings of the 8th International conference 2, 980-987, 2000. doi:10.1061/40513(279)127

5

Touran, A., "Expert system for compactor selection", J. of Transportation Engineering, 116, 338-348, 1990. doi:10.1061/(ASCE)0733-947X(1990)116:3(338)

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