The DOI matches its Crossref record; the references it cites are not checked yet.
Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development
Open accessAbstract
In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R2 value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
References 24 · Cited by 25
Research works printed but not linked: 35
- Bulkeley, 2005, Rethinking sustainable cities: Multilevel governance and the ‘urban’ politics of climate change, Environ. Politics, 14, 42, 10.1080/0964401042000310178 · DOI
- Beatley, T. (2000). Green Urbanism: Learning from European Cities, Island Press.
- Kennedy, 2007, The changing metabolism of cities, J. Ind. Ecol., 11, 43, 10.1162/jie.2007.1107 · DOI
- Irwin, L.H. (2007). The Pressuremeter and Foundation Engineering, Elsevier Science & Technology.
- Farrar, 2013, Comparison of Clegg Impact Test and California Bearing Ratio for Subgrade Strength, Geotech. Geol. Eng., 31, 1191
- Bharti, G., Hurukadli, P., Shukla, B.K., Sihag, P., Jagudi, S., and Tripathi, A. (2023). Environmental impact analysis and utilization of copper slag for stabilising black cotton soil. Mater. Today Proc., in press. · DOI
- Arya, 2023, Impact of using a blend of bagasse ash and polyester fiber in black cotton soil for improvement of mechanical and geotechnical properties of soil, Mater. Today Proc., 78, 738, 10.1016/j.matpr.2022.10.122 · DOI
- Vichare, P., and Deo, M. (2016). Soft Computing for Problem Solving, Springer.
- Mair, 2016, Predicting California Bearing Ratio (CBR) of Fine-Grained Soils for Airfields Using the DCP, Geotech. Geol. Eng., 34, 835
- Jordan, 2015, Machine learning: Trends, perspectives, and prospects, Science, 349, 255, 10.1126/science.aaa8415 · DOI · Paper page
- Taillard, 2007, An efficient simulated annealing procedure for the fleet size and mix vehicle routing problem with time windows, Transp. Sci., 41, 206
- Pickett, S.T., Cadenasso, M.L., and McGrath, B.P. (2013). Resilience in Ecology and Urban Design: Linking Theory and Practice for Sustainable Cities, Springer Science & Business Media. · DOI
- Erzin, 2016, Use of neural networks for the prediction of the CBR value of some Aegean sands, Neural Comput. Appl., 27, 1415, 10.1007/s00521-015-1943-7 · DOI
- Chao, 2018, The application of artificial neural network in geotechnical engineering, IOP Conference Series: Earth and Environmental Science, Volume 189, 022054
- Ghorbani, 2018, Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing, Soils Found., 58, 34, 10.1016/j.sandf.2017.11.002 · DOI
- Ikeagwuani, 2021, Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine, Innov. Infrastruct. Solut., 6, 199, 10.1007/s41062-021-00568-z · DOI
- Bakri, 2022, Prediction of California Bearing Ratio of Granular Soil by Multivariate Regression and Gene Expression Programming, Adv. Civ. Eng., 2022, 7426962, 10.1155/2022/7426962 · DOI
- Kelleher, J.D., Mac Namee, B., and D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies, MIT Press.
- Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media.
- Coduto, D.P., Yeung, M.R., and Kitch, W.A. (2020). Geotechnical Engineering: Principles & Practices, Pearson.
- Burton, 2010, The use of urban resources for adaptation to climate change, Local Environ., 15, 591
- Taskiran, 2010, Prediction of California bearing ratio (CBR) of fine grained soils by AI methods, Adv. Eng. Softw., 41, 886, 10.1016/j.advengsoft.2010.01.003 · DOI
- Yildirim, 2011, Estimation of California bearing ratio by using soft computing systems, Expert Syst. Appl., 38, 6381, 10.1016/j.eswa.2010.12.054 · DOI
- Suthar, 2018, Predicting CBR value of stabilized pond ash with lime and lime sludge using ANN and MR models, Int. J. Geosynth. Ground Eng., 4, 6, 10.1007/s40891-017-0125-3 · DOI
- Kurnaz, 2019, Prediction of the California bearing ratio (CBR) of compacted soils by using GMDH-type neural network, Eur. Phys. J. Plus, 134, 326, 10.1140/epjp/i2019-12692-0 · DOI
- Taha, 2019, Regression and neural network models for California bearing ratio prediction of typical granular materials in Egypt, Arab. J. Sci. Eng., 44, 8691, 10.1007/s13369-019-03803-z · DOI
- Aswed, 2020, Application of artificial neural networks in predicting subbase CBR values using soil indices data, IOP Conference Series: Materials Science and Engineering, Volume 671, 012106, 10.1088/1757-899X/671/1/012106 · DOI
- Iqbal, 2021, Smart computing models of California bearing ratio, unconfined compressive strength, and resistance value of activated ash-modified soft clay soil with adaptive neuro-fuzzy inference system and ensemble random forest regression techniques, Multiscale Multidiscip. Model. Exp. Des., 4, 207, 10.1007/s41939-021-00092-8 · DOI
- Varol, 2021, Prediction of soil-bearing capacity on forest roads by statistical approaches, Environ. Monit. Assess., 193, 1, 10.1007/s10661-021-09335-0 · DOI
- Nagaraju, T.V., Bahrami, A., Prasad, C.D., Mantena, S., Biswal, M., and Islam, R. (2023). Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique. Buildings, 13. · DOI
- Duan, 2011, Research on urban resource circulation and urban sustainable development, Procedia Environ. Sci., 5, 193
- Gunaydin, 2010, Prediction of artificial soil’s unconfined compression strength test using statistical analyses and artificial neural networks, Adv. Eng. Softw., 41, 1115, 10.1016/j.advengsoft.2010.06.008 · DOI
- Jang, 1993, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern., 23, 665, 10.1109/21.256541 · DOI
- Xiao, 2017, Multivariate sensitivity analysis based on the direction of eigenspace through principal component analysis, Reliab. Eng. Syst. Saf., 165, 1, 10.1016/j.ress.2017.03.011 · DOI
- Bassey, 2017, Correlation between CBR Values and Index Properties of Soils: A Case Study of Ibiono, Oron, and Onna in Akwa Ibom State, Resour. Environ., 7, 94
Metadata
- Authors
- Gowda, S., Kunjar, V., Gupta, A., Kavitha, G., Shukla, B. K., & Sihag, P.
- Affiliations
- Central Road Research Institute (IN)
- RASTA-Center for Road Technology, Bengaluru 560058, India
- Department of Civil Engineering, JSS Academy of Technical Education, Noida 201301, India
- Chandigarh University (IN)
- Corresponding author
- Shukla, Bishnu Kant
- ORCID
- Venue
- Urban Science
- Publisher
- MDPI AG
- Published
- 2024-01-02
- Volume
- 8
- Issue
- 1
- Pages
- 4
- ISSN
- 2413-8851 (ISSN-L)
- OpenAlex
- W4390500803
- Cited by
- 25
- References
- 24
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 3
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Urban Science (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical and construction materials studies
- Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical Engineering and Soil Stabilization
- Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical Engineering and Soil Mechanics
- Keywords
- California bearing ratio, subgrade soil, CBR prediction, adaptive neuro-fuzzy inference system, artificial neural networks, multivariate linear regression, plasticity index, maximum dry density, particle size distribution, pavement design, sensitivity analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development. https://app.cochat.ai/papers/10.3390/urbansci8010004
- The paper
- Gowda, S., Kunjar, V., Gupta, A., Kavitha, G., Shukla, B. K., & Sihag, P. (2024). Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development. Urban Science. https://doi.org/10.3390/urbansci8010004
