Soil and groundwater contamination is closely interlinked with human society because of its direct impact on population health and socioeconomic activities. The design and implementation of site remediation can be expensive, time-consuming, and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have the potential to make site remediation cost-effective with reduced human effort.

Assistant Professor Dr Raviteja KVNS, Department of Civil Engineering, has received the Best Paper Award at the Fourth International Conference on Trends and Recent Advances in Civil Engineering (TRACE) 2022 for his paper Application of artificial intelligence, machine learning and deep learning in contaminated site remediation. The conference was held at Amity University, Uttar Pradesh, on October 18 and 19, 2022. His research reports the applications of AI and ML in contaminated site remediation.

Dr Raviteja’s future research plan includes studying potential applications of various AI, ML and DL techniques for Geotechnical and Geo-environmental design and testing applications so as to reduce the labours of physical and repetitive testing and associated human effort. This further improves precision as well as aids in decision-making. He has collaborated with Prof. Krishna R Reddy, University of Illinois Chicago, for this research work.

Abstract

Soil and groundwater contamination is caused by improper waste disposal practices and accidental spills, posing a threat to public health and the environment. It is imperative to assess and remediate these contaminated sites to protect public health and the environment as well as to assure sustainable development. Site remediation is inherently complex due to the many variables involved, such as contamination chemistry, fate and transport, geology, and hydrogeology. The selection of remediation method also depends on the contaminant type and distribution and subsurface soil and groundwater conditions. Depending on the type of remediation method, many systems and operating variables can affect the remedial efficiency. The design and implementation of site remediation can be expensive, time-consuming, and may require much human effort. Emerging technologies such as Artificial Intelligence, Machine Learning, and Deep Learning have the potential to make site remediation cost-effective with reduced human effort. This study provides a brief overview of these emerging technologies and presents case studies demonstrating how these technologies can help contaminated site remediation decisions.

The Department of Civil Engineering is glad to announce that Dr Raviteja KVNS, Assistant Professor, has received the Best Paper Award for his paper titled “Compressibility Characteristics of Bentonite Amended Fly Ash Liners Exposed to Phosphate Contamination” at the 2nd International Conference RAISE 2023 (Recent Advances in Sustainable Environment) held on May 15-16, 2023. Dr Raviteja collaborated with Dr Janga Jagadeesh Kumar and Dr Krishna R Reddy, Civil, Materials and Environmental Engineering, University of Illinois Chicago for the research paper. Their cutting-edge research focused on developing alternate liner materials for waste containment systems.

Abstract

Waste containment systems like landfills, and impoundments are often lined with low permeable clays of hydraulic conductivity less than 10-7 cm/s. However, it is often challenging to get large volumes of low permeable clays near the project site. Conveyance of large volumes of clay from distant locations can be unsustainable due to the associated carbon emissions and energy costs, and not viable financially. Hence, there is a need to identify alternative liner materials without compromising on the containment capabilities. This study proposes the use of fly ash, amended with bentonite, as an alternative to the traditional liner systems. From preliminary studies, it is understood that a mixture of 80% fly ash amended with 20% bentonite is optimal to function as an effective liner material. However, the hydro-mechanical stability of liners needs to be investigated under different contamination scenarios. The present study reports the compressibility behavior of the proposed liner system under phosphate contamination, typically found in landfill leachate, impoundments, and stormwater retention ponds. One-dimensional consolidation tests were conducted on pure bentonite and fly ash with water to study the compressibility characteristics of individual materials. The optimum mix (80-20) was then tested at three different solutions exposure: water, 3.2 mg/L PO4-3-P and 12 mg/L PO4-3-P. It is depicted that the addition of 20% bentonite to fly ash did not affect the compressibility significantly, while the presence of phosphate contamination did not compromise the hydro-mechanical stability of the proposed liner system.