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Accepted and approved in partial fulfillment of the requirements for the Doctor of Philosophy degree in Civil Engineering. X Independent variable in a linear model x1 x-coordinate of the head of a fraction x2 x-coordinate of the tail of a fraction Y Dependent variable in a linear model. These reports were usually kept unused after construction of the project was completed.
Collected geotechnical survey data and empirical data can be used to create models that estimate spatial geotechnical properties at other sites without data. To address this, a new framework has been developed to assess the spatial geotechnical properties of nearby undisturbed areas by incorporating machine learning algorithms to generate models. In addition, empirical data were collected and unified to obtain calibrated empirical models that would further process the generated site-specific geotechnical data into relevant spatial geotechnical properties such as soil strength and behavior.
First of all, he wants to express his greatest thanks to the Lord, for the endless gifts of blessings.
Background of the Study
The site investigation also helps identify potential geotechnical hazards, such as liquefaction, landslides, and settlement, that may affect the stability of the structure to be constructed at the project site. It also provides information to avoid potential problems during the service life of the structure, and also helps to ensure that the project is built to meet the safety and performance requirements of the code (Peck et al., 1974). Among all the steps, the geotechnical engineering report serves as a record of site conditions and can be used as a reference in the future, such as during maintenance work, repair or any future construction in the same area.
Geotechnical engineering reports contain information on the types of soil and rock and their properties, as well as groundwater conditions and any significant information needed in the design and construction of the project, and may include potential geotechnical hazards to be addressed (Gangcuangco et al. , 2012). It is in this premise that this study was conceptualized, to collect and utilize this data, and to generate a guideline for the collection, storage and estimation of geotechnical parameters that can be used for future projects. These can be useful in identifying potential challenges or opportunities that may not be apparent during the construction of a project (Dungca & Chua, 2016).
In the pre-construction phase of a proposed project, engineers face challenges in evaluating the geotechnical parameters of a site in the absence of a site investigation report. Geostatistics (such as kriging and inverse weighted triangulation) are commonly used to interpolate geotechnical properties to create digital maps that can then be used to estimate soil characteristics in data-poor areas (Ahmed et al., 2020). However, one of the limitations of geostatistics is that it is primarily used to interpolate and estimate the distribution of numerical data.
Geostatistics does not take into account anisotropy, it only assumes isotropy of spatial data, where the spatial structure is the same in all directions. However, geotechnical properties can be highly anisotropic, meaning that the structure can change drastically depending on the direction (Shi & Wang, 2021). To overcome these limitations, other techniques such as machine learning (Liu & Macedo, 2022; Wang et al., 2020) were explored as it can learn complex patterns in data.
In addition, since geostatistics is mainly used for interpolating only numerical variables, machine learning can be used for a wide variety of tasks, such as prediction, classification, and clustering. It allows both geostatistics and machine learning to be used together to estimate geotechnical properties in locations where no survey report is available. Several machine learning algorithms are commonly used in the analysis of spatial data, such as K-Nearest Neighbor (KNN), Trees, Artificial Neural Networks (ANNs) and linear models (Tien Bui et al., 2019) among others, which are included in this research have been investigated. study.
Problem Statement
In geotechnical engineering, the application of machine learning to geotechnical spatial data estimation has not yet been explored (Joer, 2018). This geotechnical data from various sites can be collected, processed and analyzed to assess the geotechnical conditions and identify hazards within a given area. Despite the potential for compiling such data into a database, there is currently little methodical process to store and filter this data (Chadzynski et al., 2021).
Collected geotechnical survey data can be used to generate calibrated models that estimate spatial geotechnical properties at other data-free sites. Therefore, it is important to have a framework that provides a method to create a calibrated model (Wang, Zhu et al., 2021) that can help in future structural design. Once the calibrated models are developed, they can be applied to many locations in the region, allowing map delineation.
Traditionally, geotechnical engineers rely on geological maps to provide information about the distribution and characteristics of different rock types and soil types in an area (Sharifi-Mood et al., 2018). However, to supplement the information from these geological maps, it is preferable to have accompanying geotechnical maps that provide a visual representation of the region's geotechnical characteristics. Frameworks have been used in geotechnical engineering such as empirical correlation (Salsabili et al., 2022) and back-calculation (Obermeier, 1996), in which.
By maximizing geotechnical investigation data, a new framework was developed for estimating the spatial geotechnical properties of data-free areas by incorporating machine learning algorithms to create models, thereby defining maps and nomographs as representations of all properties of estimated in place and summary of calibrated empirical models, respectively.
Objectives of the Study
Linear models are good at predicting trends because they assume that the dependent variable is proportional to the change in the independent variable.
Assumptions of the Study
Scope, Limitation, and Delimitation of the Study
Furthermore, for cohesion it was limited to clay soils only, as the correlation of cohesion for sandy soils (also known as apparent cohesion) was based on moisture content which is erratic in situ, moreover, when the soil was dry, submerged or submerged. , the increased strength (apparent cohesion) disappears (Dafalla et al., 2022; Haque et al., 2013; Tian et al., 2016; Wang et al., 2021). In the code/program, only traditional models are used for classification models and regression models. For classification models, tree classification model, discriminant model, naive bayes model, nearest neighbor model and neural network model were used.
Tree regression model, linear model, quadratic model, ensemble model and neural network model were used for regression.
Significance of the Study
Other information, such as detailed particle size distribution, rock quality designation (RQD), soil carrying capacity, unrestricted compressive strength (UCS), and shear strength, was delineated. In addition, it can also support the assessment of existing structures by identifying the structures that may be at higher risk of damage during an earthquake and prioritizing them for renovation. Geotechnical engineers can use the estimated properties to make a preliminary design of foundations, such as determining the type, size, and depth of foundations appropriate for a particular project.
Consequently, it can help in the correct selection of appropriate construction methodologies and location of underground facilities. The assessed properties can help planners in the correct use of land, as they identify areas that are not suitable for construction and redirect development to a more suitable location. This research can help update codes and regulations by providing additional information in identifying strategic locations to test for geotechnical investigations.
Operational Definition of Terms
Final depth – as used in this study refers to 20 meters below sea level
Fault coordinates– are the coordinates of the head and tail of the fault line
Target location – refers to the latitude and longitude of a particular point in the map
