Dr. Basuraj Bhowmik

Assistant Professor
Department/School/Unit Name
Department of Civil Engg., IIT(BHU)
Phone No(s): .
Email: basuraj.civ@iitbhu.ac.in
Area of Interest: Structural health monitoring, real-time damage detection, online downtime detection, machine-learning algorithms for condition assessment, damping identification, FE model updating, and system identification.

Dr. Basuraj Bhowmik is an Assistant Professor in the Department of Civil Engineering at the Indian Institute of Technology (BHU). He received his Ph.D. in Structural Engineering from the Indian Institute of Technology Guwahati and graduated from National Institute of Technology Agartala with a B.Tech in Civil Engineering. Prior to IIT(BHU), Dr. Bhowmik was a Postdoctoral Research Fellow at University College Dublin, Ireland. With an Erasmus Staff Scholarship award, Basuraj was a Visiting Researcher at the University of Oulu, Finland, where he collaborated on self-sensing energy harvesting technologies. He has gained industrial experience at Siemens Power and Gas Ltd. while working as a Senior Executive in increasing the fatigue life of turbine blades using probabilistic risk estimates. He has authored a textbook titled 'Real-time structural health monitoring of vibrating systems' with the CRC Press. 

Basuraj actively works in the area of renewable energy system downtime detection, with a special emphasis on wind turbines. He is involved in developing computationally effective real-time algorithms for structural health monitoring and exploring new methodologies for data assimilation of complex systems – ranging across civil, mechanical, and aerospace systems – with a focus on sparse sensor data analytics, machine-learning algorithms, and online modal identification. His awards include the Best Reviewer Award at CTCS 2022 and the prestigious MILCA Award for Academics by the Confederation of Indian Industries. He is also an international award recipient in the form of the coveted Research Excellence Award at the Annual MaREI Symposium, Ireland

Basuraj is open to collaborations and participation in funding calls. 

Research interests: 

Structural health monitoring, real-time damage detection, online downtime detection, machine-learning algorithms for condition assessment, damping identification, FE model updating, and system identification. 


Potential Ph.D. candidates with a strong analytical background and an outstanding M.Tech degree in Civil/Mechanical/Aerospace engineering or a related field are encouraged to apply. Prior experience in writing codes in MATLAB/Python/R and finite element modeling software (ANSYS/ABAQUS) is essential for positively contributing to this group. Prior experience in machine learning, deep learning, signal processing, statistics, and learning theory will be beneficial. Prospective students interested in joining this dynamic group should directly email me with details of previous research experience, a detailed CV, and a future research plan to be conducted. Screened candidates will be advised to apply through the official portal of IIT (BHU) for consideration of their application. Collaborations with eminent researchers from the following foreign and Indian institutions would be possible: 

a)    University College Dublin, Ireland. 
b)    University of Nottingham, UK. 
c)    ETH Zurich, Switzerland.
d)    University of Oulu, Finland. 
e)    University of Surrey, UK. 
f)    Columbia University, USA. 
g)    University of Massachusetts, USA. 
h)    University Laval, Canada. 
i)    Indian Institute of Technology, Delhi. 
j)    Indian Institute of Technology, Guwahati. 

Research website: https://www.theoscarlab.com/


The OSCAR research group at IIT (BHU) is interested in combining machine-learning methods with contemporary numerical approaches for real-time diagnosis and prognosis of our aging infrastructure. These methods have the potential to expand toward renewable energy system monitoring – especially downtime detection – with a reduced order model framework. Experimental testbeds and practical field studies will be summarily conducted to validate the numerical models and arrive at a real-life computational expense study, cost-benefit analysis, and document guidelines around the process. Presently, the OSCAR research group at IIT (BHU) is looking for motivated candidates (both Ph.D. and M.Tech) to augment the notion of RT-SHM through deep learning, reinforcement tools, and digital-twin-based model studies. State-of-the-art experimental facilities through the Structural Laboratory in the Department of Civil Engineering at IIT (BHU) will be provided for the validation of numerical models.

1. Real-time structural health monitoring (RT-SHM) of built infrastructure 

Adverse operational conditions, aging, and deterioration are, among others, some of the main threats that structures and infrastructure systems are subjected to throughout their life-cycle. The technological advancements in developing sensors, capable of providing diversified measurements of structural response (e.g. accelerations, strains, temperatures, loads, etc), have led to vast scientific and practical developments in the field of Structural Health Monitoring (SHM). 

With growing demands for accurate identification of damage, asset managers rely on an online fault identification framework. Assessment of a vibrating system in relation to certain performance indices requires knowledge of the current system state considering aging and steady deterioration due to environmental factors. This necessitates the need for tracking in situ performance or health of the system by measuring data and interpreting them using application-specific knowledge. Resorting to efficient real-time vibration-based damage detection techniques, structural performance, condition, and reliability can be quantified objectively. The outcome of such analysis also furnishes the exact instant of occurrence of damage, and its localization, additionally evaluating the intensity of the damage, and providing statistics regarding the remaining service life of the vibrating system. 


Interested candidates are referred to the following for more details: 
A)    Bhowmik, B., Tripura, T., Hazra, B., & Pakrashi, V. (2019). First-order eigen-perturbation techniques for real-time damage detection of vibrating systems: Theory and applications. Applied Mechanics Reviews, 71(6), https://doi.org/10.1115/1.4044287
B)    Bhowmik, B., Tripura, T., Hazra, B., & Pakrashi, V. (2020). Robust linear and nonlinear structural damage detection using recursive canonical correlation analysis. Mechanical Systems and Signal Processing, 136, 106499, https://doi.org/10.1016/j.ymssp.2019.106499


2. Real-time downtime detection of renewable energy devices

Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. This is succeeded using critical instrumentation that is characterized by increased flexibility, modularity, and ease of use, and may consequently be exploited beyond completion of the project on further critical infrastructure components, including bridges, buildings, dams, etc. A thoroughly instrumented case study of a Wind Turbine will serve as a benchmark. Future advances in this field will include smart data harnessing, interpretation, and management for optimal life cycle assessment. Students with a background in wind turbine monitoring with a record of published research, experience in handling big data, and modelling wake-induced effects are strongly encouraged to apply. 


Present approaches for downtime detection for wind turbines include identification of classifiers that belong to either fault, excess of wind (or its lack thereof), and maintenance schedules. Considering the trade-off between misclassification errors and detection rates, detection studies are performed using wind power and speed - calibrated against available alarm classifiers. Using a kNN structure integrated within a real-time framework, applications for spatially distributed wind farms have been made and the results published in the following links: 

A)    Mucchielli, P., Bhowmik, B., Ghosh, B., & Pakrashi, V. (2021). Real-time accurate detection of wind turbine downtime-an Irish perspective. Renewable Energy, 179, 1969-1989. https://doi.org/10.1016/j.renene.2021.07.139 
B)    Mucchielli, P., Bhowmik, B., Hazra, B., & Pakrashi, V. (2020). Higher-order stabilized perturbation for recursive eigen-decomposition estimation. Journal of Vibration and Acoustics, 142(6). https://doi.org/10.1115/1.4047302

3. Single sensor-based real-time infrastructure monitoring framework 

Four key aspects of real-time condition monitoring and maintenance have been developed using the exclusive Recursive singular spectrum analysis (RSSA): (a) Filtering, (b) Enhancification, (c) Fault detection, and (d) Modal identification. As single-sensor econometrics has been long sought as a viable option for cases involving instrumentation redundancies, non-optimal sensor placement, and cost considerations, RSSA provides replication, scalability, and transferability for real-time fault detection studies. With the output vibration signals streaming in real-time, the Hankel covariance matrix is formed which filters out the noise subspace in the grouping stage. Online enhancification becomes particularly useful when the signal statistics are masked by time-varying non-stationary excitation. With applications extending to real-time passive control and aligned to current infrastructure monitoring demands worldwide, RSSA demonstrates the potential to establish as a benchmark algorithm for online condition monitoring. 

A)    Bhowmik, B., Panda, S., Hazra, B., & Pakrashi, V. (2022). Feedback-driven error-corrected single-sensor analytics for real-time condition monitoring. International Journal of Mechanical Sciences, 214, 106898. https://doi.org/10.1016/j.ijmecsci.2021.106898 
B)   Bhowmik, B., Krishnan, M., Hazra, B., & Pakrashi, V. (2019). Real-time unified single-and multi-channel structural damage detection using recursive singular spectrum analysis. Structural Health Monitoring, 18(2), 563-589. https://doi.org/10.1177%2F1475921718760483 

4. Online bridge monitoring techniques – Challenges of scour, variable damping, and rehabilitation 

Scour in railway bridges is often an important problem, especially for old bridges. In this regard, the assessment of full-scale bridges to establish scour repair adequacy or efficiency is an important question. Typically, scour weakens the bridge structure by modifying its boundary conditions. Such changes, when significant enough, can lead to changes in modal properties and vibrations measurements with respect to the ideal baseline. On the other hand, a repair attempts to restore the release in boundary conditions. Consequently, a repair also changes the modal properties and the dynamic responses. This indicates that significant and consistent changes in bridges before and after repair can indicate the adequacy and efficiency of scour repair.

As a part of the group’s research on bridge monitoring, the group has worked in instrumenting various bridges across Ireland – ranging from pedestrian bridges to train load-carrying ones – which will be continued in various parts of India as well. The data obtained from the sensors allow us to better assess the condition of existing bridges, as well as their capacity to withstand extreme loads and weather conditions. Additionally, vibration-based scour detection procedures using piezoelectric energy harvesting devices (EHD) have been conducted that promise to serve as a future benchmark in the research to come. Using one EHD attached to the central bridge pier, both scour at the pier of installation and scour at another bridge pier can be detected from the EHD voltage generated during the bridge free-vibration stage, while the harvester is attached to a healthy pier. Frequency components corresponding to harmonic loading and electrical interference arising from experiments are removed using the filter bank property of singular spectrum analysis (SSA). These frequencies can then be monitored by using harvested voltage from the energy harvesting device and successfully utilized towards SHM of a model bridge affected by scour.

Real-time estimates of natural frequencies, mode shapes, and damping of a structural system can be interpreted to its structural health. In this regard, real-time estimation of damping ratios for full-scale structures can be useful by itself or in conjunction with real-time estimates of natural frequencies.

The research group is looking for motivated candidates with experience in finite element modeling, MEMS sensors, instrumentation, and practical field studies for carrying out localized bridge inspections in various parts of the country. Additional experience in coding is desirable but not mandatory. Interested candidates should look at the following publications by this group for more details and suggested to come up with a feasible research plan. 

A)    Bhowmik, B., Hazra, B., O’Byrne, M., Ghosh, B., & Pakrashi, V. (2021). Damping estimation of a pedestrian footbridge–an enhanced frequency-domain automated approach. Journal of Vibroengineering, 23(1), 14-25. https://doi.org/10.21595/jve.2020.21577 
B)    Micu, E. A., Khan, M. A., Bhowmik, B., Florez, M. C., Obrien, E., Bowe, C., & Pakrashi, V. (2021, August). Scour Repair of Bridges Through Vibration Monitoring and Related Challenges. In International Conference of the European Association on Quality Control of Bridges and Structures (pp. 499-508). Springer, Cham. https://doi.org/10.1007/978-3-030-91877-4_57 
C)    Bhowmik, B., Quqa, S., Sause, M.G.R., Pakrashi, V., Droubi, M.G. (2021). Data Reduction Strategies. In: Sause, M.G.R., Jasiūnienė, E. (eds) Structural Health Monitoring Damage Detection Systems for Aerospace. Springer Aerospace Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-72192-3_9 

Google Scholar link: https://scholar.google.com/citations?user=4_EtmfQAAAAJ&hl=en

Book publication

1. Bhowmik B., Hazra B., and Pakrashi V., Real-time Structural Health Monitoring of Vibrating SystemsCRC Press, September 2022.

Book chapter publications

1. B Bhowmik, S Quqa, MGR Sause, V Pakrashi, MG Droubi, (2021) "Signal Processing – Structural Health Monitoring Damage Detection Systems for Aerospace". Springer (Link).
2. ​MP Limongelli, E Manoach, S Quqa, PF Giordano, B Bhowmik, V Pakrashi, A Cigada (2021) "Vibration Response-based damage detection – Structural Health Monitoring Damage Detection Systems for Aerospace". Springer (Link).

Journal publications

1. B Bhowmik, S Panda, B Hazra, and V Pakrashi (2022), "Feedback-driven error-corrected single-sensor analytics for real-time condition monitoring". International Journal of Mechanical Sciences, Elsevier, vol. 214, 106898 (Link).
2. P Mucchielli, B Bhowmik, V Pakrashi and B Ghosh (2021), "Real-time accurate detection of wind turbine downtime – an Irish perspective". Renewable Energy, Elsevier, vol. 179, pp. 1969-1989 (Link).
3. B Bhowmik, B Hazra, M O'Byrne, B Ghosh and V Pakrashi (2021), "Real-Time Damping Estimates of a pedestrian footbridge". Journal of Vibroengineering, vol. 23(1) (Link). 
4. P Mucchielli, B Bhowmik, B Hazra and V Pakrashi (2020), "Higher-order stabilized perturbation for recursive eigendecomposition estimation". Journal of Vibration and Acoustics, ASME, 142(6): 061010 (Link). 
5. B Bhowmik, T Tripura, B Hazra and V Pakrashi (2020), "Real-time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection". Journal of Sound and Vibration, Elsevier, vol. 468, pp. 115101 (Link).
6. B Bhowmik, T Tripura, B Hazra and V Pakrashi (2020), "Robust linear and nonlinear structural damage detection using recursive canonical correlation analysis". Mechanical Systems and Signal Processing, Elsevier, vol. 136, pp. 106499 (Link).
7. T Tripura, B Bhowmik, V Pakrashi and B Hazra (2020), "Real-time damage detection of degrading systems". Journal of Structural health monitoring, SAGE Publications, vol. 19(3), 810-837 (Link). 
8. B Bhowmik, T Tripura, B Hazra and V Pakrashi (2019). "First-order eigen perturbation techniques for real-time damage detection of vibrating systems: Theory and applications". Applied Mechanics Reviews, ASME, 71(6) (Link). 
9. PC Fitzgerald, AMalekjafarian, B Bhowmik, LJ Prendergast, P Cahill, CW Kim, B Hazra, V Pakrashi and EJ OBrien (2019). "Scour Damage Detection and Structural Health Monitoring of a Laboratory-Scaled Bridge Using a Vibration Energy Harvesting Device.Sensors, 19(11), 2572 (Link). 
10. B Bhowmik, M Krishnan, B Hazra & V Pakrashi (2019), "Real-time unified single and multi-channel structural damage detection using recursive singular spectrum analysis." Structural Health Monitoring, SAGE, 18 (2): 563-589 (Link).
11. M Krishnan, B Bhowmik, B Hazra & V Pakrashi (2018), "Real time damage detection using recursive principal components and time-varying auto-regressive modeling". Mechanical Systems and Signal Processing, Elsevier, 101, 549–574 (Link).
12. M Krishnan, B Bhowmik, AK Tiwari, B Hazra (2017), "Online damage detection using recursive principal component analysis and recursive condition indicators". Smart Materials and Structures, IOP Science Publications, 26(8), pp. 085017 (Link).

Conference Presentations 

1. B Bhowmik, "Improved single-sensor based modal identification using singular spectrum analysis", International Conference on Civil Engineering Trends and Challenges for Sustainability (CTCS-2021), Karnataka, India. 
2. B Bhowmik, B Hazra, V Pakrashi, "Real-time Monitoring of Built Infrastructure Systems", International Conference on Engineering Vibration (ICOEV), 2020, Aberdeen, UK.
3. V Pakrashi, W Qiu, M O’Byrne, P Cahill, B Bhowmik, B Ghosh, "Fish Farm Monitoring for Blue Growth", 4th Civil Engineering Research Association of Ireland (CERI) conference, 2020, Cork, Ireland.
4. V Pakrashi, B Bhowmik, JMG Sopena, P Mucchielli, and B Ghosh, "Wind Power Prediction and Early downtime Detection for Ireland", 4th Civil Engineering Research Association of Ireland (CERI) conference, 2020, Cork, Ireland.
5. B Bhowmik, B Hazra, and V Pakrashi, "Real-Time Damping Estimates of a pedestrian footbridge", 8th Irish Transport Research Network Conference (ITRN), 2019, Belfast, Northern Ireland.
6. B Bhowmik and B Hazra, "Real-time structural damage detection in the presence of deterministic operational power line noise", 8th International OperationalModal Analysis Conference (IOMAC), 2019, Copenhagen, Denmark.
7. B Bhowmik, T Tripura, B Hazra and V Pakrashi, "Damage detection under progressive operational degradation of structures in real-time", 8th International Operational Modal Analysis Conference (IOMAC) 2019, Copenhagen, Denmark.
8. B Bhowmik, B Hazra & V Pakrashi, "Real time structural damage detection using recursive singular spectrum analysis", 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP), 2019, Seoul, South Korea.
9. B Bhowmik, B Hazra, and V Pakrashi, "Online damage detection using recursive principal component analysis and time varying autoregressive modeling", 1st International Conference on Structural Integrity for Offshore Energy Industry, ASRANet, 2018, Aberdeen, UK.
10. B Bhowmik and B Hazra, "Smart structural damage detection using principal component analysis". 13th International Conference on Vibration Problems, ICOVP 2017, Indian Institute of Technology, Guwahati, India.
11. B Bhowmik, M Krishnan, B Hazra, and V Pakrashi, "Online Damage Detection using Recursive Principal Component Analysis". X International Conference on Structural Dynamics, EURODYN 2017, Sapienza Universita di Roma, Italy.

Current courses offered

1. CE201 - Solid Mechanics (July-Nov, 2022, Undergraduate course)

Courses taught at University College Dublin, Ireland

1. MEEN10030 - Mechanics for Engineers (May-Sep, 2020, Undergraduate course)

2. MEEN41040 - Advanced Vibrations (May-Sep, 2019, Postgraduate course)


Courses taught at Indian Institute of Technology Guwahati

1. ME101 - Engineering Mechanics (Jan-May, 2017, Undergraduate course)

2. CE513 - Statistical Methods in Civil Engineering (July-Dec, 2017, Postgraduate course)

3. CE608 - Reliability-based Structural Design (Jan-May, 2018, Postgraduate course)

1. Best Reviewer Award -  International Conference on Civil Engineering Trends and Challenges for Sustainability (CTCS 2022)
2. MILCA Award for Academics - International Confederation of Indian Industries (CII 2021)
3. Research Excellence Award - SFI Research Centre for Energy, Climate and Marine research and innovation, Ireland (MaREI 2020)
4. Erasmus Staff Scholarship Award - University of Oulu, Finland (2019)
5. MHRD Merit Scholarship - Indian Institute of Technology Guwahati (2015-2018)

1. Management Committee Member of Cost Action (CA 18203) ’Optimising Design for Inspection’ – leading to a Springer recommendation book on Structural Health Monitoring for Aerospace systems.
2. Topical Advisory Panel Member, Journal of Applied Mechanics. 
3. Reviewer for ASCE Journals of Structural Engineering and Bridge Engineering, Elsevier Journals of Applied Energy, Engineering Structures, Sound and Vibration, Mechanical Systems and Signal Processing, ASME Journals of Vibration and Acoustics, Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering, IEEE Sensors Journal, SAGE Structural Health Monitoring, Springer Journals of Earthquake Engineering and Experimental Techniques, among others.
4. Grant Reviewer for the Icelandic Research Fund (Rannis).
5. Engineering Category Judge of The Global Undergraduate Awards.
6. Member of the Review Panel for the International Conference on Civil Engineering Trends and Challenges for Sustainability (CTCS-2021), India.
7. Society Member of the American Society of Civil Engineers (ASCE) 2018-Present.