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Improved resistance prediction and reliability for bridge pile foundation in shales through optimal site investigation plans

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  • Oluwatuyi, Opeyemi E.
  • Ng, Kam
  • Wulff, Shaun S.

Abstract

Site investigation (SI) provides relevant subsurface information for designing piles driven into soft rocks or intermediate geomaterials (IGMs) such as shale. However, uncertainties in geological and ground (geomaterial) conditions pose challenges to accurately predicting pile resistance and ensuring design reliability. This study used multinomial categorical prediction (MCP) and conditional simulation on a random field to develop an optimal SI plan (OSIP). The OSIP aims to minimize geomaterial uncertainties, improve design efficiency, and enhance the economic benefits of pile foundations. The proposed method integrates OSIP with static analysis (SA) methods. This integration enhances the calibrated resistance factor from the load and resistance factor design (LRFD) by improving the quality of geomaterial information used in pile resistance prediction. The effectiveness of the proposed method is demonstrated using a database consisting of 22 steel end-bearing H-piles that are driven into weathered shales and load tested in Kansas, Iowa, and Wyoming. By incorporating OSIP into pile design, the prediction error of end-bearing resistance is decreased by 25.6%, and the LRFD resistance factor for redundant piles is increased by 20%. Furthermore, a cost-benefit analysis of the pile design reveals an average saving of approximately $39,410 per foundation. Break-even analysis further supports OSIP as a cost-effective SI plan option for enhancing pile design.

Suggested Citation

  • Oluwatuyi, Opeyemi E. & Ng, Kam & Wulff, Shaun S., 2023. "Improved resistance prediction and reliability for bridge pile foundation in shales through optimal site investigation plans," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023003903
    DOI: 10.1016/j.ress.2023.109476
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    References listed on IDEAS

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    1. Zhang, Chi & Wang, Zeyu & Shafieezadeh, Abdollah, 2021. "Error Quantification and Control for Adaptive Kriging-Based Reliability Updating with Equality Information," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    2. Argyroudis, Sotirios A. & Mitoulis, Stergios Aristoteles, 2021. "Vulnerability of bridges to individual and multiple hazards- floods and earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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    4. Liu, Wenli & Chen, Elton J. & Yao, Erlei & Wang, Yanyu & Chen, Yangyang, 2021. "Reliability analysis of face stability for tunnel excavation in a dependent system," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
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    6. Deng, Jian, 2022. "Probabilistic characterization of soil properties based on the maximum entropy method from fractional moments: Model development, case study, and application," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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    1. Jiadong, Qiu & Ohl, Joy P. & Tran, Trung-Tin, 2024. "Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Jerez, Danko J. & Chwała, M. & Jensen, Hector A. & Beer, Michael, 2024. "Optimal borehole placement for the design of rectangular shallow foundation systems under undrained soil conditions: A stochastic framework," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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