Developing Nomographs for the Unit Weight of Soils

Abstract

Engineers have created increasingly complex correlations based on laboratory and field tests. Over time, geotechnical engineering modeling techniques have evolved from simple analytical methods to complex numerical modeling techniques. Nomographs are traditional computational tools that have been widely employed in engineering. Combining nomographs with computational tools such as numerical models and machine learning algorithms can lead to better outcomes. Thus, this study aimed to develop a nomograph for geotechnical engineering that incorporates machine learning, specifically for the unit weight of soil. Four calibrated models were developed to determine the unit weight of soil: the moist unit weight of coarse-grained soil, the saturated unit weight of coarse-grained soil, the moist unit weight of fine-grained soil, and the saturated unit weight of fine-grained soil. An uncertainty test was conducted for the data used. Our results indicated a strong positive relationship to most of the models. The generated nomographs were tested in Malabon, a city in Metro Manila, where a low unit weight of soil was determined. This low unit weight was validated by the predominance of alluvial deposits and the shallow groundwater table, which soften and weaken the soil.

Keywords

• nomograph
• unit weight
• machine learning
• Philippines
• estimate