Suryakanta Biswal

Dr Suryakanta Biswal


Areas of specialism

Uncertainty modelling and quantification, finite element model updating, damage detection, large scale structural testing, prediction of long-time deformation in concrete structures, losses in prestressed concrete structures

My qualifications

PhD with MSc in Engineering
Indian Institute of Science Bangalore
College of Engineering and Technology, Bhubaneswar

Previous roles

06 July 2017 - 13 April 2018
Assistant Professor
SSN College of Engineering, Chennai

My publications


Biswal S., Wang Y. (2019) Optimal sensor placement strategy for the identification of local bolted connection failures in steel structures,Proceedings of the 2019 International Conference on Smart Infrastructure and Construction (ICSIC 2019) ICE Publishing
Failure of bolted connections in steel structures may result in catastrophic effects. Many algorithms in existing literature use
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.
Zhang Tong, Biswal Suryakanta, Wang Ying (2019) Deep Convolutional Neural Network for Condition Identification of Connections in Steel Structures,Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM 2019)
The deep learning technologies have transformed many research areas with accuracy
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.
Zhang Tong, Biswal Suryakanta, Wang Ying (2020) SHMnet: Condition assessment of bolted connection with beyond human-level performance,Structural Health Monitoring 19 (4) pp. 1188-1201 SAGE Publications
Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at