Sajib Saha

Bio: Sajib Saha is an academic researcher from Texas A&M Transportation Institute. The author has contributed to research in topics: Subgrade & Artificial neural network. The author has an hindex of 5, co-authored 12 publications receiving 76 citations. Previous affiliations of Sajib Saha include Texas A&M University.

Non-invasive imaging of Young's modulus and Poisson's ratio in cancers in vivo.

Abstract: Alterations of Young's modulus (YM) and Poisson's ratio (PR) in biological tissues are often early indicators of the onset of pathological conditions. Knowledge of these parameters has been proven to be of great clinical significance for the diagnosis, prognosis and treatment of cancers. Currently, however, there are no non-invasive modalities that can be used to image and quantify these parameters in vivo without assuming incompressibility of the tissue, an assumption that is rarely justified in human tissues. In this paper, we developed a new method to simultaneously reconstruct YM and PR of a tumor and of its surrounding tissues based on the assumptions of axisymmetry and ellipsoidal-shape inclusion. This new, non-invasive method allows the generation of high spatial resolution YM and PR maps from axial and lateral strain data obtained via ultrasound elastography. The method was validated using finite element (FE) simulations and controlled experiments performed on phantoms with known mechanical properties. The clinical feasibility of the developed method was demonstrated in an orthotopic mouse model of breast cancer. Our results demonstrate that the proposed technique can estimate the YM and PR of spherical inclusions with accuracy higher than 99% and with accuracy higher than 90% in inclusions of different geometries and under various clinically relevant boundary conditions.

Use of an Artificial Neural Network Approach for the Prediction of Resilient Modulus for Unbound Granular Material

Abstract: The resilient modulus (MR) is a fundamental material property that has a direct effect on the design and analysis of pavement structures. Many regression models have been developed previously to pr...

Prediction of Soil-Water Characteristic Curve for Unbound Material Using Fredlund–Xing Equation-Based ANN Approach

Abstract: Most of the existing soil-water characteristic curve (SWCC) prediction models do not have a high level of prediction accuracy. The R2 values of these model predictions range from 0.1 to 0.6...

Rapid estimation of resilient modulus of subgrade soils using performance-related soil properties

Abstract: This study aims at developing an efficient and accurate methodology to estimate the resilient modulus of subgrade soils. First, a new resilient modulus model incorporating stress dependence and moi...

35 Years of (AI) in Geotechnical Engineering: State of the Art

Abstract: It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in geotechnical engineering, during those years many (AI) techniques were developed based in mathematical, statistical and logical concepts, but the breakthrough occurs by mimicking the natural searching and optimization algorithms. This huge development in (AI) techniques reflected on the geotechnical engineering problems. In this research, 626 paper and thesis published in the period from 1984 to 2019 concerned in applying (AI) techniques in geotechnical engineering were collected, filtered, arranged and classified with respect to subject, (AI) technique, publisher and publishing date and stored in a database. The extracted information from the database were tabulated, presented graphically and commented. The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researches.

Research and applications of artificial neural network in pavement engineering: a state-of-the-art review

Abstract: Given the great advancements in soft computing and data science, artificial neural network (ANN) has been explored and applied to handle complicated problems in the field of pavement engineering. This study conducted a state-of-the-art review for surveying the recent progress of ANN application at different stages of pavement engineering, including pavement design, construction, inspection and monitoring, and maintenance. This study focused on the papers published over the last three decades, especially the studies conducted since 2013. Through literature retrieval, a total of 683 papers in this field were identified, among which 143 papers were selected for an in-depth review. The ANN architectures used in these studies mainly included multi-layer perceptron neural network (MLPNN), convolutional neural network (CNN) and recurrent neural network (RNN) for processing one-dimensional data, two-dimensional data and time-series data. CNN-based pavement health inspection and monitoring attracted the largest research interest due to its potential to replace human labor. While ANN has been proved to be an effective tool for pavement material design, cost analysis, defect detection and maintenance planning, it is facing huge challenges in terms of data collection, parameter optimization, model transferability and low-cost data annotation. More attention should be paid to bring multidisciplinary techniques into pavement engineering to tackle existing challenges and widen future opportunities.