Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning

Shreyanshu Padelkar; Raju Narwade; Karthik Nagarajan; Rajashri Narwade

doi:doi:10.11648/j.ajce.20251306.14

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Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning

Shreyanshu Padelkar^*

, Raju Narwade

, Karthik Nagarajan

, Rajashri Narwade

Published in American Journal of Civil Engineering (Volume 13, Issue 6)

Received: 5 November 2025 Accepted: 26 November 2025 Published: 20 December 2025

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Abstract

This study proposes a novel approach for real-time strength monitoring and risk assessment of building structures by leveraging machine learning for concrete compressive strength prediction. The assessment of the prevailing Reinforced Concrete (RC) buildings for a seismic damage is a hard structural engineering trouble and a key problem for disaster mitigation and resilience. The seismic damage evaluation of those structures aids in figuring out whether or not the buildings can be used effectively after the earthquake by knowing the chance of damage degrees. We developed a machine learning model capable of analyzing various concrete mix parameters, including the amount of cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and the age of the concrete. The model predicts the compressive strength, a crucial indicator of a RCC column's structural integrity. In structural engineering, assessing the seismic susceptibility of the existing reinforced concrete (RC) buildings is a critical task that is essential to resilience and catastrophe preparedness. Seismic Risk Assessments (SRA) of these structures help determine whether a building is safe for post-earthquake use by tracking the likelihood of damage. approach allows for continuous assessment of a building's structural health, enabling proactive identification of potential risks. The inclusion of this technology into current monitoring systems offers building managers and engineers actionable insights which help them make informed decisions about maintenance and repair requirements. This research establishes new methods for proactive and effective building structure risk assessment which improves safety and extends the life span of constructed environments.

Published in	American Journal of Civil Engineering (Volume 13, Issue 6)
DOI	10.11648/j.ajce.20251306.14
Page(s)	362-372
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Concrete Compressive Strength, Strength Monitoring, Risk Assessment, Machine Learning, Structural Health, Reinforced Concrete

References

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APA Style

Padelkar, S., Narwade, R., Nagarajan, K., Narwade, R. (2025). Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning. American Journal of Civil Engineering, 13(6), 362-372. https://doi.org/10.11648/j.ajce.20251306.14

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ACS Style

Padelkar, S.; Narwade, R.; Nagarajan, K.; Narwade, R. Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning. Am. J. Civ. Eng. 2025, 13(6), 362-372. doi: 10.11648/j.ajce.20251306.14

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AMA Style

Padelkar S, Narwade R, Nagarajan K, Narwade R. Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning. Am J Civ Eng. 2025;13(6):362-372. doi: 10.11648/j.ajce.20251306.14

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@article{10.11648/j.ajce.20251306.14,
  author = {Shreyanshu Padelkar and Raju Narwade and Karthik Nagarajan and Rajashri Narwade},
  title = {Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning},
  journal = {American Journal of Civil Engineering},
  volume = {13},
  number = {6},
  pages = {362-372},
  doi = {10.11648/j.ajce.20251306.14},
  url = {https://doi.org/10.11648/j.ajce.20251306.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20251306.14},
  abstract = {This study proposes a novel approach for real-time strength monitoring and risk assessment of building structures by leveraging machine learning for concrete compressive strength prediction. The assessment of the prevailing Reinforced Concrete (RC) buildings for a seismic damage is a hard structural engineering trouble and a key problem for disaster mitigation and resilience. The seismic damage evaluation of those structures aids in figuring out whether or not the buildings can be used effectively after the earthquake by knowing the chance of damage degrees. We developed a machine learning model capable of analyzing various concrete mix parameters, including the amount of cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and the age of the concrete. The model predicts the compressive strength, a crucial indicator of a RCC column's structural integrity. In structural engineering, assessing the seismic susceptibility of the existing reinforced concrete (RC) buildings is a critical task that is essential to resilience and catastrophe preparedness. Seismic Risk Assessments (SRA) of these structures help determine whether a building is safe for post-earthquake use by tracking the likelihood of damage. approach allows for continuous assessment of a building's structural health, enabling proactive identification of potential risks. The inclusion of this technology into current monitoring systems offers building managers and engineers actionable insights which help them make informed decisions about maintenance and repair requirements. This research establishes new methods for proactive and effective building structure risk assessment which improves safety and extends the life span of constructed environments.},
 year = {2025}
}

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TY  - JOUR
T1  - Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning
AU  - Shreyanshu Padelkar
AU  - Raju Narwade
AU  - Karthik Nagarajan
AU  - Rajashri Narwade
Y1  - 2025/12/20
PY  - 2025
N1  - https://doi.org/10.11648/j.ajce.20251306.14
DO  - 10.11648/j.ajce.20251306.14
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 362
EP  - 372
PB  - Science Publishing Group
SN  - 2330-8737
UR  - https://doi.org/10.11648/j.ajce.20251306.14
AB  - This study proposes a novel approach for real-time strength monitoring and risk assessment of building structures by leveraging machine learning for concrete compressive strength prediction. The assessment of the prevailing Reinforced Concrete (RC) buildings for a seismic damage is a hard structural engineering trouble and a key problem for disaster mitigation and resilience. The seismic damage evaluation of those structures aids in figuring out whether or not the buildings can be used effectively after the earthquake by knowing the chance of damage degrees. We developed a machine learning model capable of analyzing various concrete mix parameters, including the amount of cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and the age of the concrete. The model predicts the compressive strength, a crucial indicator of a RCC column's structural integrity. In structural engineering, assessing the seismic susceptibility of the existing reinforced concrete (RC) buildings is a critical task that is essential to resilience and catastrophe preparedness. Seismic Risk Assessments (SRA) of these structures help determine whether a building is safe for post-earthquake use by tracking the likelihood of damage. approach allows for continuous assessment of a building's structural health, enabling proactive identification of potential risks. The inclusion of this technology into current monitoring systems offers building managers and engineers actionable insights which help them make informed decisions about maintenance and repair requirements. This research establishes new methods for proactive and effective building structure risk assessment which improves safety and extends the life span of constructed environments.
VL  - 13
IS  - 6
ER  -

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Author Information

Shreyanshu Padelkar

Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, India

Contact Email

http://orcid.org/0009-0005-6987-9759
Raju Narwade

Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, India

Contact Email

http://orcid.org/0000-0003-2349-0172
Karthik Nagarajan

Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, India

Contact Email

http://orcid.org/0000-0002-2306-0327
Rajashri Narwade

Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, India

http://orcid.org/0009-0002-1827-8919

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APA Style

Padelkar, S., Narwade, R., Nagarajan, K., Narwade, R. (2025). Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning. American Journal of Civil Engineering, 13(6), 362-372. https://doi.org/10.11648/j.ajce.20251306.14

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ACS Style

Padelkar, S.; Narwade, R.; Nagarajan, K.; Narwade, R. Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning. Am. J. Civ. Eng. 2025, 13(6), 362-372. doi: 10.11648/j.ajce.20251306.14

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AMA Style

Padelkar S, Narwade R, Nagarajan K, Narwade R. Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning. Am J Civ Eng. 2025;13(6):362-372. doi: 10.11648/j.ajce.20251306.14

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@article{10.11648/j.ajce.20251306.14,
  author = {Shreyanshu Padelkar and Raju Narwade and Karthik Nagarajan and Rajashri Narwade},
  title = {Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning},
  journal = {American Journal of Civil Engineering},
  volume = {13},
  number = {6},
  pages = {362-372},
  doi = {10.11648/j.ajce.20251306.14},
  url = {https://doi.org/10.11648/j.ajce.20251306.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajce.20251306.14},
  abstract = {This study proposes a novel approach for real-time strength monitoring and risk assessment of building structures by leveraging machine learning for concrete compressive strength prediction. The assessment of the prevailing Reinforced Concrete (RC) buildings for a seismic damage is a hard structural engineering trouble and a key problem for disaster mitigation and resilience. The seismic damage evaluation of those structures aids in figuring out whether or not the buildings can be used effectively after the earthquake by knowing the chance of damage degrees. We developed a machine learning model capable of analyzing various concrete mix parameters, including the amount of cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and the age of the concrete. The model predicts the compressive strength, a crucial indicator of a RCC column's structural integrity. In structural engineering, assessing the seismic susceptibility of the existing reinforced concrete (RC) buildings is a critical task that is essential to resilience and catastrophe preparedness. Seismic Risk Assessments (SRA) of these structures help determine whether a building is safe for post-earthquake use by tracking the likelihood of damage. approach allows for continuous assessment of a building's structural health, enabling proactive identification of potential risks. The inclusion of this technology into current monitoring systems offers building managers and engineers actionable insights which help them make informed decisions about maintenance and repair requirements. This research establishes new methods for proactive and effective building structure risk assessment which improves safety and extends the life span of constructed environments.},
 year = {2025}
}

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TY  - JOUR
T1  - Real Time Strength Monitoring of Concrete and Risk Assessment of Building Structures Using Machine Learning
AU  - Shreyanshu Padelkar
AU  - Raju Narwade
AU  - Karthik Nagarajan
AU  - Rajashri Narwade
Y1  - 2025/12/20
PY  - 2025
N1  - https://doi.org/10.11648/j.ajce.20251306.14
DO  - 10.11648/j.ajce.20251306.14
T2  - American Journal of Civil Engineering
JF  - American Journal of Civil Engineering
JO  - American Journal of Civil Engineering
SP  - 362
EP  - 372
PB  - Science Publishing Group
SN  - 2330-8737
UR  - https://doi.org/10.11648/j.ajce.20251306.14
AB  - This study proposes a novel approach for real-time strength monitoring and risk assessment of building structures by leveraging machine learning for concrete compressive strength prediction. The assessment of the prevailing Reinforced Concrete (RC) buildings for a seismic damage is a hard structural engineering trouble and a key problem for disaster mitigation and resilience. The seismic damage evaluation of those structures aids in figuring out whether or not the buildings can be used effectively after the earthquake by knowing the chance of damage degrees. We developed a machine learning model capable of analyzing various concrete mix parameters, including the amount of cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and the age of the concrete. The model predicts the compressive strength, a crucial indicator of a RCC column's structural integrity. In structural engineering, assessing the seismic susceptibility of the existing reinforced concrete (RC) buildings is a critical task that is essential to resilience and catastrophe preparedness. Seismic Risk Assessments (SRA) of these structures help determine whether a building is safe for post-earthquake use by tracking the likelihood of damage. approach allows for continuous assessment of a building's structural health, enabling proactive identification of potential risks. The inclusion of this technology into current monitoring systems offers building managers and engineers actionable insights which help them make informed decisions about maintenance and repair requirements. This research establishes new methods for proactive and effective building structure risk assessment which improves safety and extends the life span of constructed environments.
VL  - 13
IS  - 6
ER  -

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