Estimating soil classification via quantitative and qualitative field testing for use in constructing compressed earth blocks

摘要

Compressed earth blocks (CEBs) represent a cost-effective and sustainable building material for construction in low-income areas. One challenge with CEB construction is the dependence of CEB unit strength on the character of soil, which varies based on location. Soil classification standards require laboratory testing; therefore standardized assessment of soil characteristics is difficult in the field. Typically, field tests provide primarily qualitative data collected by builders of varying experience; some crude quantitative data can also be collected. This research seeks to establish a framework for relating qualitative and quantitative field data to standardized soil classification methods. The framework is a neural evaluation system comprising artificial neural networks to compare input-output soil classification data and establish relationships between field data, laboratory data, and ultimately standardized soil classification. Soil samples from many regions around and outside of the United States were classified using results from both ASTM tests and field tests.