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A hybrid method for strand looseness identification in post-tensioned system using FEM and ANN

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Abstract

While prestressing technology is increasingly implemented in civil infrastructures worldwide, on-site identification of loosened strands and estimation of looseness extent remain a challenging issue. This study presents a hybrid method combining finite element model (FEM) with artificial neural networks (ANNs) to assess strand looseness in post-tensioned systems. The proposed method is capable of locating single or multiple loosened strands and estimating prestress (PS) loss extent. To deal with on-site anchorage structures with low accessibility and limited data acquisition, the networks are trained by the numerical strain datasets obtained from a FE model instead of the experimental datasets. The numerical evaluation shows that the proposed method can accurately locate the loosened strands and estimate the looseness severity, even for untrained patterns. The present method also gives good predictions for actual strand looseness in a tested nine-strand anchorage. The axial stress at the bottom of the anchor head is found suitable for strand looseness identification. Due to its high accuracy and ease of implementation, the proposed method shows the great potential for on-site assessment of prestressed structures.

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Acknowledgements

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.

Funding

This research received no external funding.

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Authors and Affiliations

  1. Urban Infrastructure Faculty, Mien Tay Construction University, Vinh Long, 890000, Vietnam

    Ngoc-Loi Dang & Ngoc-Tuong-Vy Phan

  2. Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc City, Ho Chi Minh City, 700000, Vietnam

    Duc-Duy Ho

  3. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, 700000, Vietnam

    Duc-Duy Ho

  4. Department of Ocean Engineering, Pukyong National University, Busan, 48513, Korea

    Jeong-Tae Kim

  5. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Thanh-Canh Huynh

  6. Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Thanh-Canh Huynh

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  1. Ngoc-Loi Dang
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  2. Ngoc-Tuong-Vy Phan
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  4. Jeong-Tae Kim
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Correspondence to Jeong-Tae Kim or Thanh-Canh Huynh.

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Appendix A. Effect of different loading patterns on prediction accuracy

Appendix A. Effect of different loading patterns on prediction accuracy

Due to the influence of elastic interaction, the strand prestress will reduce or increase by the preload of another strand and different loading patterns may affect the strand prestress distribution. Appendix Table

Table 7 Axial stress variation in the anchor head under different loading patterns of Strand 7
Full size table

7 shows the axial stress variations measured at AB1–AB8 (close to Strand 1–Strand 8, respectively) under different loading patterns of Strand 7. It is obvious that the strand prestress was redistributed, as evidenced by the axial stress variations on the surface of the anchor head. The looseness of Strand 7 led to a significant increase in the prestress of Strand 6 and Strand 8 (close to Strand 7) while the prestress of Strand 2–4 (opposite to Strand 6–8, see Fig. 15) was reduced. We further investigated the influence of elastic interaction on the prediction accuracy of the A-ANN (Network 11). The PS-loss prediction results for different loadings of Strand 7 are shown in Appendix Table

Table 8 The prediction accuracy of the A-ANN (Network 11) under different loading patterns
Full size table

8. For the three examined loading patterns, the accuracy of the network is quite similar, regardless the PS-loss severity.

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Dang, NL., Phan, NTV., Ho, DD. et al. A hybrid method for strand looseness identification in post-tensioned system using FEM and ANN. J Civil Struct Health Monit 13, 1287–1311 (2023). https://doi.org/10.1007/s13349-023-00704-6

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