Dr Suryakanta Biswal joined the Department of Civil and Environmental Engineering as a Research Fellow, and is working in Structural Health Monitoring, a project funded by the Engineering & Physical Sciences Research Council.
Suryakanta has a PhD along with a Masters degree from Indian Institute of Science Bangalore. His thesis was on “Uncertainty based damage identification and prediction of long-time deformation in concrete structures”. In this work algorithms were developed for identifying different types of damages in concrete structures, when various types of uncertainties (both aleatory and epistemic) were present in the measurement, in the finite element model, and in the prior distribution of the parameters in the finite element model. The damages considered in this work were loss of stiffness, loss of mass, and loss of bond between concrete and steel. The other major section of this work dealt with predicting the long-time prestress losses in post-tensioned concrete beams and slabs, given the uncertainties in the measurements taken during the short-time period.
Suryakanta has worked as a Research Fellow in the BRNS/DAE, India Project entitled “Development of a Model for Evaluating Prestress Losses Considering Creep & Shrinkage Losses in Concrete & Relaxation Losses in Steel over 100 Years”, from SEPTEMBER 2012 TO March 2015. The last one year he has been working as Lecturer at SSN College of Engineering, Chennai.
Suryakanta has years of experience in scientific programming (mainly Matlab), and commercial software for finite element analysis (mainly ANSYS and ABAQUS), and is happy to help other colleagues with this.
Areas of specialism
Affiliations and memberships
modal information of a structure to identify damage in that structure, based on the data acquired from accelerometers which record the vibration
time histories at different points on the structure. The location of these points may have significant effects on the quality of the acquired data,
and thus the identified modal information. In this paper, a distance measure based Markov chain Monte Carlo algorithm is proposed to
determine the optimal locations for the accelerometers, and the optimal location of the impact hammer if need. Different damage cases with
various combinations of bolt failures are considered in this study. Failures at various levels are simulated by loosening the bolts in a predefined
order. To compare the efficiency of the proposed method, the total effect of various damage cases on the accelerations at the optimal locations
are calculated for the proposed method and a state-of-the-art method from the existing literature. The results demonstrate the efficiency of the
proposed strategy in locating the accelerometers, which can produce data that are more sensitive to the bolted connection failures.
levels that the traditional methods are not comparable with. Recently, they have received
increasing attention in the structural health monitoring (SHM) domain. In this paper,
we aim to develop a new deep learning algorithm for structural condition monitoring
and to evaluate its performance in a challenging case, bolt loosening damage in a frame
structure. First, the design of a one-Dimensional Convolutional Neural Network (1DCNN)
is introduced. Second, a series of impact hammer tests are conducted on a steel
frame in the laboratory under ten scenarios, with bolts loosened at different locations
and quantities. For each scenario, ten repeated tests are performed to provide enough
training data for the algorithm. Third, the algorithm is trained with different quantities
of training data (from one to seven test data for each scenario), and then is tested with
the rest test data. The results show that the proposed 1D-CNN with three convolutional
layers provide reliable identification results (over 95% accuracy) with sufficient training
data sets. It has the potential to transform the SHM practice.