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Dr. Samim Mustafa
Assistant Professor
Department of Civil Engineering IIT(BHU)
samim.civ@iitbhu.ac.in
+916392667711
Area of Interest: 
Bayesian inference, Finite Element Model Updating, Structural Health Monitoring, Bridge Weigh-In-Motion, System Identification, Damping Analysis, Distributed Optical Fiber Sensing, Seismic Response Analysis of Bridges

Greetings

Dr. Samim Mustafa is an Assistant Professor in the department of Civil Engineering of Indian Institute of Technology (BHU) Varanasi. He holds a Doctor of Philosophy in Civil Engineering from the School of Science of Saitama University, Japan. He got his Master of Technology in Civil Engineering with specialization in Structures Engineering from IIT Guwahati, and Bachelor of Engineering (First Class with Honours) in Civil Engineering from Bengal Engineering and Science University Shibpur (presently IIEST). Prior to joining IIT (BHU), he worked as a Post-doctoral Researcher in Advanced Research Laboratories of Tokyo City University. He also has industrial experience in Larsen & Toubro Ltd. where he worked as a Graduate Engineer Trainee for more than two years while being posted in Bokaro Steel Plant for the up-gradation of Blast Furnace II project. 

RESEARCH INTERESTS

Bridge weigh-in-motion, Bayesian approach and uncertainty estimation, Structural condition assessment, Damping Analysis, Earthquake analysis of bridges, FE-model updating, System identification, MEMS sensors, Distributed optical fiber, Finite Element Methods, Deep learning in SHM

MAILING ADDRESS
Dr. Samim Mustafa
Department of Civil Engineering
IIT (BHU) Varanasi
Varanasi - 221005, Uttar Pradesh, India

OPPORTUNITIES
Prospective students with the experience of using finite element modeling software (ABAQUS/ANSYS/SAP) and knowledge of writing codes in MATLAB/Python may contact me directly via Email with details of previous research experience, future research plan and CV. For deciding future research plans, it is advisable to go through the different research themes stated on my homepage under "Research Works". Candidates with new idea(s) in the domain of structural health monitoring and uncertainty estimation, beyond the mentioned themes listed in my homepage, are also welcomed. Please include a statement of research with your new idea(s) while sending an Email to me. Potential candidates will be contacted immediately, if found suitable and they will be advised to apply through the official portal of IIT (BHU) [Link] for the consideration of their application.
Best wishes,
SM

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1. Bayesian Model Updating
Problem Statement

Mathematical models are widely used to predict the actual behavior of real systems in nearly all fields of engineering. However, such mathematical models are constructed on the basis of highly idealized engineering designs and data that may not truly represent all the aspects of an actual structure. As a result, significant discrepancies may exist between the predicted finite element model (FEM) and the actual built structure. The sources of uncertainties in such models can be classified into three categories: measurement errors representing uncertainties in the measured vibration data, modeling errors resulting from idealization and assumptions in FEM construction, and statistical uncertainties in the model parameters.

Ongoing Works

An efficient and robust Bayesian probabilistic approach was developed for FEM updating that accounts for various uncertainties and utilizes incomplete modal data (modal frequencies and partial mode shapes) identified by a limited number of sensors. A new objective function was introduced for Bayesian model updating that does not require any scaling or normalization of mode shapes because the likelihood function for mode shapes is formulated based on the cosine of the angle between the analytical and experimentally identified mode shapes. To validate the proposed approach, an initial MATLAB-based FEM of an existing steel truss bridge was updated using the identified modal parameters from the measured vibration data. The proposed framework was also found to be robust against measurement errors. The damage-detection capability of the proposed model-updating framework was then investigated by considering the data from a simulated damaged bridge and the experimental data from a damaged span of the same bridge with partial fractures on one of the diagonal members.
 

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.


2. Vibration-based Structural Health Monitoring (SHM)
Problem Statement
There are some serious issues with the practical application of vibration-based SHM to detect local damage. Many vibration-based damage-detection approaches have been proposed in the literature. Most of these studies on damage detection have been mainly based on change in modal frequencies and/or change in mode shape–related indices. However, the problem related to low sensitivity of damage features due to local damages remains a concern for the practical application of these methodologies. In contrast, it has been recognized that damping is more sensitive to local damages, especially due to cracks or some internal changes in the structural property.
Ongoing Works
An analytical framework for vibration-based SHM was introduced using an energy-based damping evaluation (EBDE). The damage detection by the proposed EBDE was carried out by estimating the contribution of modal damping ratios from different structural elements utilizing experimentally identified modal damping ratios, and estimating modal strain and modal potential energies from an updated finite-element (FE) model of the structure under consideration. Model updating was performed using modal frequencies and mode shapes that are generally not sensitive to local damage. The advantage of using damping as a damage indicator is that the damping change in global modes affected by the local damage can be identified with a small number of sensors. A previous study reported that the studied bridge with damage at the local diagonal member showed a significant increase in the damping of global vibration mode of the structure. The proposed vibration-based SHM approach could be promising in detecting damage at the local level when the problem related to low sensitivity of frequencies and mode shapes due to local damage remains a concern, and damage detection by change in stiffness parameters using FE model updating utilizing data from a large number of sensors is not practically feasible due to the limitation of budget and time.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.


3. Monitoring System for Seismic Damage Detection
Problem Statement
For the long-term monitoring of bridges, it is important to reduce the size, cost, and power consumption of the monitoring system. For the purpose of selecting sensors and their arrangement logically to evaluate the soundness of a bridge immediately after an earthquake, it is essential to understand not only the bridge behavior but also the damaged structural components, the failure modes, and the sequence of the failures should also be properly identified. With the rubber bearing system, the behavior of plate girder bridges is usually complex under extreme loading conditions such as during an earthquake. Most of the prior studies concerning the seismic behavior of girder bridges have mainly focused on individual components rather than considering bridge behavior as a whole.
Ongoing Works
A methodology for the selection of sensors and their arrangement was developed for detecting seismic damage in an in-service steel plate girder bridge system. In this study, a detailed span-based model was developed for the finite element simulation including the effect of the rubber bearing and piers, and the damage control by the side blocks. The finite element dynamic simulation was carried out with input earthquake acceleration to investigate the seismic behavior and grasp the damageable parts during an earthquake. Based on the results of finite element dynamic simulation, a fault tree analysis was carried out to reveal more about the bridge behavior, the failure modes, and the occurrence of damage. It was found that the side block, the bearing stiffener, and the horizontal bracing on the fixed side of the bridge are most important to be monitored for the evaluation of soundness of a plate girder bridge immediately after an earthquake. Finally, a sensor arrangement for the bridge was proposed based on the analysis results. In this monitoring system, the optical time-domain reflectometer (OTDR) is placed in all bearings which are fixed in the transverse direction by the side blocks to measure the relative displacement between the superstructure and the substructure.
 

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.


4. Bridge Weigh-In-Motion
Problem Statement
Overweight vehicles can lead to serious damages and accelerate the degradation of pavements and various bridge components. In particular, the fatigue damage in steel bridges, which are caused by the long-term cumulative effects of traffic loads, can significantly shorten the service life of bridges The static bridge weigh-in-motion (BWIM) systems are mostly based on strain measurements and are particularly suited for stiff short-span bridges. Recently, the BWIM systems based on acceleration measurements are developed for long-span bridges because of the portability and low-cost of accelerometers as compared to strain gauges. Although, these BWIM systems can estimate the gross vehicle weights (GVWs) with high accuracy, but they fail to identify the weights of individual axles accurately especially for vehicles with closely spaced axles.
Ongoing Works
  • An iterative linear optimization problem (ILOP) was proposed to accurately identify the individual axle weights and GVWs of vehicles traversing a bridge. The proposed method used the bridge displacement responses as the measured responses which were determined from the recorded acceleration data. The information about the vehicle speed, number of axles and axle spacings were obtained by identifying the peaks in the recorded acceleration data. The effectiveness and accuracy of the proposed method were demonstrated through field tests using the four-axle test vehicles with closely spaced axles. The results showed that the axle weights of vehicles with closely spaced axles could be identified with much better accuracy by the proposed method as compared to classic BWIM systems which are based on Moses’ original algorithm.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

  • Bayesian updating provides a coherent framework for assimilating data into models. Bayesian bridge weigh-in-motion (BBWIM), which combines Bayesian updating and BWIM, is proposed. BBWIM can estimate not only the representative value of axle weights but also the uncertainty of the estimated value and the correlation among estimates. Uncertainties in estimated axle weight are quantitatively discussed with a simple two-axle problem. It is shown that the estimated weights of closely spaced axles have large uncertainty. BBWIM is applied to the measured data for an actual bridge. It is shown that additional information, in the form of a weak constraint on axle weight, namely, that closely spaced axles have similar weights, can reduce the un- certainty of estimated axle weights. ​

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.


5. Retrofitting
Problem Statement
A large number of existing girder bridges are susceptible to structural deterioration due to their progressive aging. For example, Tokyo metropolitan expressway has a total length of bridge network of about 320 km and about 63% of those are in-operation for over 30 years (Shutoko CSR Report. 2018). Moreover, elevated bridges (viaducts) accounts for 76% of the total length and about 40% of elevated bridges are having steel piers. Therefore, a majority of elevated girder bridges are in a need of repair and rehabilitation to deal with serious damage caused by the aging of structures and severe usage.
Ongoing Works
Orthotropic steel decks (OSDs) are being used increasingly to replace old deteriorated reinforced concrete decks (RCDs). A numerical investigation was carried out to examine the effect of reduction in superstructure weight on the seismic resistance of the bridges when an existing RCD was being replaced by an OSD. Firstly, a detailed FE-model of a plate girder bridge with RCD was developed and then, another FE-model was constructed by replacing the RCD with the model of an OSD. Then, an eigenvalue analysis was carried out for each FE-model and their natural frequencies and the mode shapes were compared in order to understand the influence of deck replacement on the global vibrational characteristics of the bridge. After that, the effects of deck replacement on the seismic performance of the whole bridge system was investigated by performing a series of non-linear dynamic analysis with input earthquake data in the longitudinal and transverse directions separately and simultaneously. Based on the results of seismic response analysis, the differences in response behavior and damage level due to the replacement of deck were evaluated.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Link: https://link.springer.com/article/10.1007/s13296-020-00370-0
6. Distributed Optical Fiber Sensing
Description
As a better alternative to traditional sensors, optical fiber sensors have been developed for industrial and medical applications. Their advantages include immunity to electromagnetic interference, chemical stability, electrical isolation, low sensitivity to temperature variations, and durability. Additionally, their small size, flexibility, and light weight makes them easily embeddable into any kind of host. As a result, several optical fiber sensor technologies have emerged recently, including grating-based sensors and DOFS based on Raman, Brillouin, and Rayleigh scattering. Fiber Bragg grating (FBG) sensors that measure the periodic variation of the refractive index in the fiber core, produce stronger reflection signals within an optical fiber than DOFS. However, a major drawback of FBG interrogation systems is that the maximum number of FBG sensors is limited to a few tens or less. Moreover, the sensors must be custom designed and manufactured, while DOFS are made from standard unaltered optical fibers. For a quasi-distributed FBG system, the number of sensors and their spacing are determined by the number and location of Bragg gratings etched into the cable, whereas the entire fiber serves as a sensor in systems using DOFS.
Ongoing Works
To investigate the optimal embedment depth for DOFS for pavement monitoring, a series of field tests was conducted by considering three embedment depths (10, 30, and 40 mm) and three moving loads (pedestrian, car and truck). The main objectives were to examine the performance of DOFS embedded in the pavement and investigate its suitability and sensitivity for obtaining information about moving loads at various depths from the road surface. Among the existing backscattering light technology, Rayleigh scattering-based Optical Frequency Domain Reflectometry (OFDR) was chosen in order to achieve a measurement of high-speed, high range, and good repeatability with a very high spatial resolution of 2.56 mm, all of which are necessary for monitoring dynamic responses due to moving loads. In addition, a data-processing method was developed based on a combination of a Hampel identifier and a low-pass filter, which proved to be effective in removing outliers and high-frequency noise from the raw measurement data, respectively. The results showed that it is not only possible to detect a moving load on a road, but that a wide range of information can also be obtained from the strains measured by DOFS, including the type of load, its speed, its weight, the number of axles, the axle spacings (in the case of a vehicle) and the traffic flow. With low maintenance costs, DOFS based on Rayleigh scattering OFDR could be used for collection of accurate and reliable traffic data when embedded in a pavement.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Link: https://doi.org/10.1016/j.yofte.2021.102705
7. Bridge Influence Line Identification and Damage Detection
Description
Due to the ever increasing nature of traffic loads, many existing bridges which are in-operation for two decades or more are under enormous strain already. Therefore, it is indispensable to know the actual in-service condition of bridges for the evaluation of their performances, safety and the remain life. To achieve this goal, the bridge influence line (IL) can play a key role as it represents a unique characteristic of a bridge. The bridge IL describes an important static property of the bridge that shows the variation of reaction or any internal forces at a certain location when a bridge is subjected to a moving unit load. As an IL gives a direct relationship between the load and the response, it is widely used in design of bridges, structural condition assessment, model correction and bridge weigh-in-motion system (BWIM) system.
Ongoing Works
The method to obtain an accurate influence line (IL) from the direct measurement is an important research topic for structural condition assessment, model correction and bridge weigh-in-motion (BWIM) system. The two most common approaches used for the identification of IL are the time-domain (TD) method and the frequency-domain (FD) method. Despite having a similar mathematical framework, the TD and the FD methods are treated as two different methods by the researchers working on this field. This paper presents a detailed theoretical demonstration to show that the two methods discussed above are nothing but the same. The two methods were compared experimentally by using field measurement data on an existing steel girder bridge which were obtained by using three calibration trucks (CTs) with different axle weights and axle configurations. Although the ILs identified by the two methods were apparently different, but a theoretical insight into the frameworks revealed that the TD and FD methods are basically the same and a seeming difference between the two methods is due to the inherent assumptions involved in the discrete Fourier transform (DFT) such as the assumption of cyclic nature of analysis interval. Finally, a method to obtain an accurate influence line has been outlined.

Photo of the Remarkables mountain range in Queenstown, New Zealand.

Link: https://doi.org/10.1016/j.istruc.2021.05.082


JOURNALS (*Corresponding Author, IF: Impact Factor)
  1. Yoshida I*, Mustafa S, Sekiya H, Maruyama K. Bayesian Bridge Weigh-In-Motion considering Dynamic Response in Observation Noise. Structural Safety, (Elsevier) (Under Revision), Jan 2022.
  2. Mustafa S*, Sekiya H, Morichika S, Maeda I, Takaba S and Hamajima A. Monitoring Internal Strains in Asphalt Pavements Under Static Loads Using Embedded Distributed Optical Fiber. Optical Fiber Technology (Elsevier), 68, 102829, Feb 2022. DOI: 10.1016/j.yofte.2022.102829 (H-index: 62, IF: 2.51)
  3. Mustafa S*, Sekiya H, Maeda I, Takaba S, Hamajima A. Identification of External Load Information Using Distributed Optical Fiber Sensors Embedded in an Existing Road Pavement. Optical Fiber Technology, (Elsevier), 67, 102705, Sep 2021. DOI: 10.1016/j.yofte.2021.102705 (H-index: 62, IF: 2.51)
  4. Mustafa S*, Yoshida I, Sekiya H. An investigation of Bridge influence line identification using time-domain and frequency-domain methods. Structures (Elsevier), 33, 2061-2065, June 2021. DOI: 10.1016/j.istruc.2021.05.082 (H-index: 22, IF: 2.98)
  5. Mustafa S*, Sekiya H, Hamajima A, Maeda I, Hirano S. Effects of Speeds and Weights of Travelling Vehicles on the Road Surface Temperature. Transportation Engineering (Elsevier), 5, 100077, May, 2021. DOI: 10.1016/j.treng.2021.100077 (H-index: 41, IF: 2.64)
  6. Yoshida I*, Sekiya H, Mustafa S. Bayesian Bridge Weigh-In-Motion and Uncertainty Estimation. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(1), 04021001, Jan 2021. DOI: 10.1061/AJRUA6.0001118. (H-index: 12, IF: 1.86)
  7. Mustafa S*, Sekiya H, Hirano S, Miki C. Iterative Linear Optimization Method for Bridge Weigh-In-Motion Systems Using Accelerometers. Structure and Infrastructure Engineering (Taylor & Francis), 17:9, 1245-1256, Aug 2020. DOI: 10.1080/15732479.2020.1802490. (H-index: 48, IF: 2.62)
  8. Mustafa S*, Sekiya H, Hayama M, Miki C. Effects of Redecking from RC Deck to Orthotropic Steel Deck on Seismic Resistance of Elevated Girder Bridges. International Journal of Steel Structures, 20, 1393-1404, June 2020. DOI: 10.1007/s13296-020-00370-0. (H-index: 17, IF: 1.35)
  9. Mustafa S*, Sekiya H, Miki C. Determining the Location of Sensors for Seismic Damage Detection in Steel Girder Bridges with Elastomeric Bearings. Journal of Vibration and Control, 26(19-20), 1-12, Feb 2020. DOI: 10.1177/1077546320905176. (H-index: 63, IF: 3.095)
  10. Mustafa S*, Miki C. Design of Rupture Strength of Side Blocks in Elevated Steel Girder Bridges with Elastomeric Bearings. International Journal of Steel Structures, Springer, 20, 885–896, March 2020. DOI: 10.1007/s13296-020-00329-1 (H-index: 17, IF: 1.35)
  11. Mustafa S*, Matsumoto Y, Yamaguchi H. Vibration-based health monitoring of an existing truss bridge using energy-based damping evaluation, Journal of Bridge Engineering (ASCE), 23(1), 04017114-1-15, Jan 2018. DOI: 10.1061/(ASCE)BE.1943-5592.0001159. (H-index: 66, IF: 2.196)
  12. Mustafa S*, Matsumoto Y. Bayesian model updating and its limitations for detecting local damage of an existing truss bridge, Journal of Bridge Engineering (ASCE), 22(7), 04017019-1-14, Mar 2017. DOI: 10.1061/(ASCE)BE.1943-5592.0001044. (H-index: 66, IF: 2.196)
  13. Mustafa S, Debnath N, Dutta A*. Bayesian probabilistic approach for model updating and damage detection for a large truss bridge, International Journal of Steel Structures, Springer, Vol. 15, No 2, pp. 473-485, June 2015. DOI: 10.1007/s13296-015-6016-3 (H-index: 17, IF: 1.35)
  14. Mustafa S. Bridge Influence line identification using Bayesian method considering multiple signals. (In-preparation)
  15. Mustafa S, Yoshida I, Sekiya H. Evaluation of Fatigue Damage in Steel Girder Bridges Using Displacement Influence Lines. (In-preparation)
CONFERENCE PROCEEDINGS
  1. Mustafa S, Dammika AJ, Matsumoto Y, Yamaguchi H, Yoshioka T. A Bayesian Probabilistic Approach for Finite Element Model Updating Utilizing Vibration Data Measured in an Existing Steel Truss Bridge. Proceeding of Structural Health Monitoring of Intelligent Infrastructure: Torino, Italy, Vol. RS3, pp. 114-123, July, 2015.
  2. Mustafa S, Dutta A. Bayesian Probabilistic Approach for model updating and damage detection, In proceeding of Vienna Congress on Recent Advances in Earthquake Engineering and Structural Dynamics, Vienna, Austria, Vol. 119, August, 2013.
  3. Mustafa S, Matsumoto Y. Model updating of a steel truss bridge using Bayesian probabilistic approach. In proceeding of 17th International Summer Symposium of JSCE, Okayama, Japan, Vol. CS2, No. 003, pp. 5-6, September, 2015.
  4. Mustafa S, Matsumoto Y. An energy-based damping evaluation for interpretation of damping increase due to damage in an existing steel truss bridge. In proceeding of 18th International Summer Symposium of JSCE, Sendai, Japan, Vol. CS2, No. 005, September, 2016.
  5. Mustafa S, Matsumoto Y, Yamaguchi H. An Energy-based Damping Evaluation for the Local Damage Detection of an Existing Steel Truss Bridge, In Proceeding of Structural Health Monitoring of Intelligent Infrastructure: Brisbane, Australia, Vol. RS5, 575-585, 5-8 December 2017.
  6. Maruyama K, Yoshida I, Sekiya H, Mustafa S. Improvement of estimation accuracy by BWIM considering autocorrelation of observation error, In proceeding of 23rd International Summer Symposium of JSCE, Tokyo, D1, September, 2021. (ONLINE)
SEMINAR PRESENTATIONS
  1. Mustafa S, Matsumoto Y. Structural Health Monitoring of Truss Bridges Using Vibration Measurement. 5th ISAJ Symposium: Advances in Natural Sciences & Technologies, Indian Embassy Auditorium, Tokyo, December, 2014. (Poster)
  2. Mustafa S. Bayesian-based Bridge weigh-in-motion using MEMS accelerometers. The 163rd TCU-ARL Seminar, International Workshop on Data-driven Infrastructure Maintenance and Risk Management, Tokyo, Japan, September 24, 2020.
TECHNICAL REPORTS
  1. Road Surface Temperature Monitoring Using Infrared Sensor. A project with Toyota. Tokyo, Japan March 2020.
  2. Movement Detection Using Distributed Optical Fiber. A project with Toyota. Tokyo, Japan, June 2020.
  • To be updated soon

Ongoing
EVEN Semester:
    1. CE101: Basic Surveying
          Materials:
              Module 1: Introduction, Types of Surveys and Maps, Scales, Accuracy and Errors [Link]
              Module 2: Chain Surveying [Link] and Compass Surveying [Link]
              Module 3: Levelling and Contouring [Link]
              Module 8: Photogrammetry Engineering [Link]
    2. CE102: Surveying Laboratory
ODD Semester

  • To be updated soon

Courses Taught
At IIT (BHU)
- To be updated soon

Elsewhere

  • CE402: Structural Dynamics
  • CE202: Strength of Materials
  • Post-doctoral researcher, Tokyo City University (2018 - 2022)
  • MEXT fellowship by Japanese government (2013 - 2016)
  • Research Assistantship during Masters, IIT Guwahati (2011 - 2013) 
  • First Class Honours by Bengal Engineering and Science University Shibpur (2008)