Affiliation:
1. CSIR-Central Road Research Institute, New Delhi 110025, India
2. RASTA-Center for Road Technology, Bengaluru 560058, India
3. Department of Civil Engineering, JSS Academy of Technical Education, Noida 201301, India
4. Department of Civil Engineering, Chandigarh University, Mohali 140413, India
Abstract
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.
Subject
Pollution,Urban Studies,Waste Management and Disposal,Environmental Science (miscellaneous),Geography, Planning and Development
Reference41 articles.
1. United Nations (2018). The World’s Cities in 2018, United Nations, Department of Economic and Social Affairs, Population Division.
2. Rethinking sustainable cities: Multilevel governance and the ‘urban’ politics of climate change;Bulkeley;Environ. Politics,2005
3. Beatley, T. (2000). Green Urbanism: Learning from European Cities, Island Press.
4. The changing metabolism of cities;Kennedy;J. Ind. Ecol.,2007
5. California Department of Transportation (2019). California Bearing Ratio (CBR) Test Procedure.