Soil Unconfined Compressive Strength Testing From LIBS and Moisture Data
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Solution Overview
Problem
Conventional methods for determining unconfined compressive strength (UCS) of soil are time-consuming, costly, and prone to inaccuracies, while existing in situ methods face limitations in applicability, efficiency, and potential adverse effects on soil quality.
Innovation Solution
A field portable device employing laser-induced breakdown spectroscopy (LIBS) combined with a decision tree regressor and adaptive boosting classifier to predict UCS, using spectral emission intensities, bulk density, and water content of soil samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional UCS measuring methods (manual digging, physical property measurement setup, GPR, ERT) are used, then measurement precision is improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The patent replaces mechanical and physical measurement systems (manual digging, physical property measurement setup, GPR, ERT) with a laser-based optical system. The LIBS device uses laser-induced breakdown spectroscopy to analyze soil samples, substituting time-consuming mechanical methods with rapid optical spectroscopy that provides UCS predictions within seconds while maintaining measurement precision through spectral analysis of soil elements.
Solution Approach 2:
The patent changes the measurement parameter from direct mechanical strength testing to spectral emission intensity analysis. By measuring the intensity of spectral lines from excited soil elements (such as Si, Al, Fe, Ca, Mg, Na, K) and correlating these spectral parameters with UCS through machine learning models, the system achieves rapid assessment without the time-consuming process of physical compression testing.
2Loss of time
If in situ soil testing methods are used, then loss of time is reduced, but measurement precision and reliability deteriorate due to limited applicability and soil quality degradation
Solution Approach 1:
The patent creates a spectral copy of the soil sample's elemental composition through LIBS analysis. Instead of physically disturbing or transporting the soil sample, the system generates a spectral fingerprint that captures the elemental information needed for UCS prediction. This copying approach maintains soil integrity while providing accurate measurement data for machine learning-based UCS estimation.
3Device complexity
If simple statistical equations are used for soil analysis, then device complexity is reduced, but measurement precision deteriorates due to oversimplification and assumptions
Solution Approach 1:
The patent introduces machine learning models (random forest, support vector machine, neural network) as intermediaries between the spectral data and UCS prediction. These intermediary algorithms process the complex spectral emission intensities and identify non-linear relationships between elemental compositions and soil strength, achieving high prediction accuracy (R² > 0.95) without requiring complex physical measurement systems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device achieves high accuracy in predicting UCS with R2-scores up to 99.03%, overcoming the limitations of conventional methods and providing a robust, efficient, and accurate field-based solution.
Implementation Method 1
The spectrometer is configured to perform laser induced breakdown spectroscopy on the soil sample and generate spectral emission intensities of the soil sample
Data Source
AI summary
A field portable device for determining the unconfined compressive strength of a soil sample includes a sample holder, a heating device, a scale, a spectrometer, and a microprocessor. The sample holder receives a soil sample. The heating device dries the soil sample for a specified time. The scale measures a weight of the soil sample and a dried weight of the soil sample. The spectrometer performs laser induced breakdown spectroscopy on the soil sample and generates spectral emission intensities of the soil sample. The microprocessor calculates a bulk density and a water content of the soil sample, actuates the spectrometer to generate the spectral emission intensities. The microprocessor applies the spectral emission intensities, the bulk density and the water content to a trained machine learning regressor combined with an adaptive boosting classifier to predict the unconfined compressive strength of the soil sample.