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A platform for research: civil engineering, architecture and urbanism
Damage Detection for a Cantilevered Steel I-Beam through Deep-Learning Methods: LSTM, Multivariate Time-Series Transformer, and LSTM-Based Autoencoder
https://orcid.org/0009-0005-4124-6271
The structural integrity of steel trusses and I-beams is of vital importance for preventing the potential collapse of steel bridges when subjected to extraordinary forces. Thus, identifying damage to I-beams, which cannot be noticed in typical inspections, based on their measured response, would enable early damage detection, and would trigger the necessary mitigation measures to restore the structural integrity of the bridge. This investigation built a vast database of structurally damaged cantilever I-beams, in which openings of various degrees and locations were placed along the beams to emulate reductions in stiffness. Both damaged and undamaged I-beams were modeled using Abaqus software, facilitated by Python scripting. Three deep-learning algorithms were trained, validated and tested with the healthy and damaged I-beam cases: long short-term memory (LSTM), a LSTM-based autoencoder, and multivariate time-series transformers (MTTs), for which the input data consisted of acceleration responses recorded at specific points on the top flange of both undamaged and damaged I-beams subjected to harmonic dynamic loads. To enhance adaptation for field monitoring data, random normal noise was introduced into the acceleration responses before running the machine learning (ML) damage identification algorithms. The three algorithms demonstrated exceptional ability to accurately distinguish between the damaged and the undamaged I-beams. Furthermore, the location of the damage on the beam was identified by the LSTM and MTT algorithms, which had the best accuracy for damage localization. Finally, a comparative analysis of the three algorithms was conducted to clarify the optimal quantity of data points required to attain reliable results.
Damage Detection for a Cantilevered Steel I-Beam through Deep-Learning Methods: LSTM, Multivariate Time-Series Transformer, and LSTM-Based Autoencoder
https://orcid.org/0009-0005-4124-6271
The structural integrity of steel trusses and I-beams is of vital importance for preventing the potential collapse of steel bridges when subjected to extraordinary forces. Thus, identifying damage to I-beams, which cannot be noticed in typical inspections, based on their measured response, would enable early damage detection, and would trigger the necessary mitigation measures to restore the structural integrity of the bridge. This investigation built a vast database of structurally damaged cantilever I-beams, in which openings of various degrees and locations were placed along the beams to emulate reductions in stiffness. Both damaged and undamaged I-beams were modeled using Abaqus software, facilitated by Python scripting. Three deep-learning algorithms were trained, validated and tested with the healthy and damaged I-beam cases: long short-term memory (LSTM), a LSTM-based autoencoder, and multivariate time-series transformers (MTTs), for which the input data consisted of acceleration responses recorded at specific points on the top flange of both undamaged and damaged I-beams subjected to harmonic dynamic loads. To enhance adaptation for field monitoring data, random normal noise was introduced into the acceleration responses before running the machine learning (ML) damage identification algorithms. The three algorithms demonstrated exceptional ability to accurately distinguish between the damaged and the undamaged I-beams. Furthermore, the location of the damage on the beam was identified by the LSTM and MTT algorithms, which had the best accuracy for damage localization. Finally, a comparative analysis of the three algorithms was conducted to clarify the optimal quantity of data points required to attain reliable results.
Damage Detection for a Cantilevered Steel I-Beam through Deep-Learning Methods: LSTM, Multivariate Time-Series Transformer, and LSTM-Based Autoencoder
https://orcid.org/0009-0005-4124-6271
The structural integrity of steel trusses and I-beams is of vital importance for preventing the potential collapse of steel bridges when subjected to extraordinary forces. Thus, identifying damage to I-beams, which cannot be noticed in typical inspections, based on their measured response, would enable early damage detection, and would trigger the necessary mitigation measures to restore the structural integrity of the bridge. This investigation built a vast database of structurally damaged cantilever I-beams, in which openings of various degrees and locations were placed along the beams to emulate reductions in stiffness. Both damaged and undamaged I-beams were modeled using Abaqus software, facilitated by Python scripting. Three deep-learning algorithms were trained, validated and tested with the healthy and damaged I-beam cases: long short-term memory (LSTM), a LSTM-based autoencoder, and multivariate time-series transformers (MTTs), for which the input data consisted of acceleration responses recorded at specific points on the top flange of both undamaged and damaged I-beams subjected to harmonic dynamic loads. To enhance adaptation for field monitoring data, random normal noise was introduced into the acceleration responses before running the machine learning (ML) damage identification algorithms. The three algorithms demonstrated exceptional ability to accurately distinguish between the damaged and the undamaged I-beams. Furthermore, the location of the damage on the beam was identified by the LSTM and MTT algorithms, which had the best accuracy for damage localization. Finally, a comparative analysis of the three algorithms was conducted to clarify the optimal quantity of data points required to attain reliable results.
Damage Detection for a Cantilevered Steel I-Beam through Deep-Learning Methods: LSTM, Multivariate Time-Series Transformer, and LSTM-Based Autoencoder
J. Comput. Civ. Eng.
Sadeghian, Ehsan (author) / Dragomirescu, Elena (author) / Inkpen, Diana (author)
2025-03-01
Article (Journal)
Electronic Resource
English
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