Soil Classification Uscs Aashto Calculator
Our soil & sediment mechanics calculator computes soil classification uscs aashto accurately. Enter measurements for results with formulas and error
Reviewed for accuracy by Daniel Agrici, Founder & Lead Developer
Soil Classification Uscs Aashto Calculator
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Formula: USCS: Based on grain size (>50% coarse or fine) + Atterberg limits. AASHTO: GI = (F-35)[0.2+0.005(LL-40)] + 0.01(F-15)(PI-10)
Worked example โ USCS: SC (Clayey sand), AASHTO: A-2-6
Formula
USCS: Based on grain size (>50% coarse or fine) + Atterberg limits. AASHTO: GI = (F-35)[0.2+0.005(LL-40)] + 0.01(F-15)(PI-10)
The USCS first divides soils by the percentage passing the No. 200 sieve (75 micron). Coarse-grained soils are further split by gravel vs sand predominance and fines character. Fine-grained soils are classified on the plasticity chart using the A-line equation PI = 0.73(LL-20). The AASHTO Group Index is a weighted function of fines content, liquid limit, and plasticity index that rates subgrade quality from 0 (excellent) to 20 (very poor).
Worked Examples
Example 1: Silty Sand Classification
Problem:Classify a soil with 15% gravel, 55% sand, 30% fines, LL = 35, PI = 12.
Solution:Fines < 50%, so coarse-grained Sand > Gravel, so sand group Fines > 12%, PI > 7: Clayey sand USCS: SC AASHTO: Fines = 30%, LL = 35, PI = 12 Group A-2-6 (clayey sand)
Result:USCS: SC (Clayey sand), AASHTO: A-2-6
Example 2: High Plasticity Clay
Problem:Classify a soil with 5% gravel, 10% sand, 85% fines, LL = 65, PI = 40.
Solution:Fines > 50%: fine-grained soil LL > 50: high plasticity PI = 40 > 0.73*(65-20) = 32.85: above A-line USCS: CH (Fat clay) AASHTO: A-7 with high Group Index
Result:USCS: CH (Fat clay), AASHTO: A-7
Frequently Asked Questions
What is the USCS soil classification system?
The Unified Soil Classification System (USCS) was developed by Arthur Casagrande in the 1940s and standardized as ASTM D2487. It classifies soils into groups based on grain size distribution and Atterberg limits. Coarse-grained soils (more than 50 percent retained on the No. 200 sieve) are subdivided into gravels (G) and sands (S), then further by gradation (W for well-graded, P for poorly-graded) or fines type (M for silt, C for clay). Fine-grained soils (more than 50 percent passing the No. 200 sieve) are classified by the Casagrande plasticity chart into clays (C), silts (M), and organics (O) with low (L) or high (H) plasticity.
How does the AASHTO classification system differ from USCS?
The AASHTO system was developed specifically for highway and pavement engineering by the American Association of State Highway and Transportation Officials. It classifies soils into groups A-1 through A-7, with A-1 being the best subgrade material and A-7 the worst. It uses a Group Index (GI) that combines fines content, liquid limit, and plasticity index into a single number indicating suitability as pavement subgrade. Lower GI values indicate better performance. Unlike USCS which focuses on engineering behavior, AASHTO is optimized for predicting road subgrade quality.
What is the Casagrande plasticity chart and how is it used?
The plasticity chart plots the plasticity index (PI) on the vertical axis against the liquid limit (LL) on the horizontal axis. The A-line, defined by PI = 0.73(LL - 20), separates clays (above the line) from silts (below the line). The U-line, PI = 0.9(LL - 8), represents the approximate upper bound for natural soils. Soils plotting to the left of LL = 50 are low plasticity (L suffix), while those to the right are high plasticity (H suffix). This chart is the primary tool for classifying fine-grained soils and provides insight into compressibility and swelling potential.
Why is soil classification important for engineering projects?
Soil classification provides a standardized language for engineers to communicate soil properties and expected behavior without detailed testing at every location. It helps predict drainage characteristics, bearing capacity, settlement potential, and suitability as construction material. For example, well-graded gravels (GW) make excellent foundation and road base material, while high-plasticity clays (CH) are problematic due to high compressibility and swelling. Classification guides preliminary design decisions, helps estimate construction costs, and determines what additional testing is needed for detailed analysis.
What is the difference between classification, regression, and clustering?
Classification predicts discrete categories (spam or not spam). Regression predicts continuous values (house price). Both are supervised learning (require labeled data). Clustering groups similar data points without labels (unsupervised). Use classification for categories, regression for numbers, and clustering for discovering natural groupings in data.
References
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