Assessment of decision-making factors affecting dump truck allotment: An artificial neural network approach

Date of Publication

2007

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Civil Engineering with Spec in Construction Technology & Management

Subject Categories

Civil Engineering

College

Gokongwei College of Engineering

Department/Unit

Civil Engineering

Thesis Adviser

Jason Maximino Ongpeng

Defense Panel Chair

Bernardo A. Lejano

Defense Panel Member

Danilo C. Terante
Alexis Fillone

Abstract/Summary

This study deals with the assessment of decision making factors affecting dump truck allotment in the construction of gasoline service stations in the Philippines. Such factors considered in this study were the following: (a) location of project site, (b) project duration, (c) distance of the source materials (such as soil) from the the project site (d) volume of backfill, (e) depth of backfill, and (f) total project lot area. Upon conducting data gathering, the group came to a conclusion that the most significant decision making factors among those mentioned above were the volume of backfill and distance of source materials from project site. Moreover, throughout the course of the study, the group also found out that there are actually two methods of site development processes affecting dump truck allotment the Gradual and Rapid methods from which the factor of project duration is taken into account.

The group utilized the MATLAB software, particularly of the supervised network approach in ANN analysis using the Learning Vector Quantization (LVQ) technique. This was performed in order to recognize patterns among the volume of backfill and distance of source materials so as to arrive with the best pattern-based mathematical models for each method.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU14442

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

xii, 86 leaves : ill. (some col.) ; 28 cm.

Keywords

Dump truck; Dumping appliances; Service stations; Petroleum industry and trade

Embargo Period

2-2-2022

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