Developing a Framework for Estimating Spatial Geotechnical Properties with a case of Metro Manila, Philippines

Date of Publication

12-1-2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Civil Engineering

College

Gokongwei College of Engineering

Department/Unit

Civil Engineering

Thesis Advisor

Jonathan R. Dungca

Defense Panel Chair

Mary Ann Q. Adajar

Defense Panel Member

Erica Elice S. Uy

Andres Winston C. Oreta

Lessandro Estelito O. Garciano

Raymond Girard R. Tan

Abstract/Summary

As each construction project requires to conduct a geotechnical site investigation, reports are also generated consequently. These reports were just usually stored unused after the construction of the project is completed. It is in this premise that this study was conceptualized, to collect and maximize these data, and to generate a guideline for collecting, storing and estimating geotechnical parameters, which may be used for future projects. Collected geotechnical investigation data and empirical data can be repurposed for generating models that estimate spatial geotechnical properties at other sites with no data. Therefore, it is also significant to have a methodology that provides a procedure for creating models that can assist in the future design of structures. To address this, a new framework was developed for estimating the spatial geotechnical properties of nearby undisturbed areas by incorporating machine learning algorithms to create models. Through a code/program, models for ground elevation, groundwater elevation, SPT N-value, fines content, soil type, and atterberg limits have been developed. Moreover, empirical data were collected and unified to come up with calibrated empirical models that would further process the generated site-specific geotechnical data into important spatial geotechnical properties such as soil strength and soil behavior. Maps and profiles were delineated by deploying the models in multiple locations throughout a case region. Nomographs were also created as a summary of the calibrated models. To test the developed framework, it was applied to Metro Manila, Philippines as a case study with overall accuracy rate is 82%, indicating a very strong relationship between the models and the estimated parameters.

Abstract Format

html

Language

English

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Embargo Period

4-25-2023

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