Research
Research interests
Ultra high performance concrete (UHPC)
Design of Concrete Structures
Reliability Engineering
Design under Uncertainty
Bayesian Inference
Structural Health Monitoring
If you are interested in joining us, kindly contact Prof. Sahil Bansal (sahil.iitdelhi@gmail.com) with your CV and research interests.
Structural Reliability Analysis
In any practical situation there are several parameters, such as loadings, structural parameters, geometric parameters, operation conditions, etc. which are uncertain. In the presence of these uncertainties achieving absolute safety is impossible. The principles of structural reliability have been developed to compute the probability of failure as a quantitative measure of structural safety. Using the principles of structural reliability, the level of reliability of an existing structure designed as per the existing standards can be evaluated. It can also be used for developing a reliability-based design criterion, in the form of code calibration to compute the partial safety factors for an accepted level of reliability.
Looking for motivated candidates with excellent background in Design of Concrete Structures.
Ultra High-Performance Concrete (UHPC)
Ultra-high-performance concrete (UHPC) is a class of advanced cementitious materials with greater strength, tensile ductility, and durability properties when compared to general or high-performance concrete. UHPC is concrete that has a minimum specified compressive strength of 120-150 MPa with specified durability, tensile ductility and toughness requirements.
Looking for motivated candidates with excellent background in concrete technology.
Optimization under Uncertainty
Structural optimization methods are commonly utilized in the design of engineering structures to improve structural performance while lowering costs. Robust Design Optimization (RDO) of structures has emerged as an important methodology that seeks to determine a design that is insensitive to input variations.
Looking for motivated candidates with basic knowledge of Dynamics, Probability and Programming.
Bayesian Model Updating
There always exist modeling errors and uncertainties associated with the process of constructing a mathematical model of a system arising either because of incomplete knowledge or simplifying assumptions made during the modeling of the physical problem. The ability to quantify the uncertainness accurately and appropriately is essential for a robust prediction of future response and computation of measures such as robust reliability, etc.. In this context, a fully probabilistic Bayesian model updating approach provides a robust and rigorous framework to characterize modeling uncertainties.
Looking for motivated candidates with basic knowledge of Dynamics, Probability and Programming.
Optimal Sensor Placement
The process of constructing a mathematical description of a system is known as modelling. A model, no matter how precise, will never be an exact replica of a real physical system. The model updating process is an inverse problem in which the goal is to reduce the gap between model predictions and experimental evidence. However, because of measurement and modelling errors, the updated model parameters typically involve uncertainty. It is thus of crucial importance to quantify and reduce the uncertainty in the updated model parameters. In this regard, Optimal Sensor Placement, that is, selecting the optimal location for a given number of sensors for model updating, is frequently applied for maximizing the data information so that the modelling parameter uncertainties can be reduced.
Looking for motivated candidates with basic knowledge of Dynamics, Probability and Programming.
