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

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.

Automated summary

This study developed an optimal site investigation plan to minimize geological and ground uncertainties in pile design, improve efficiency, and enhance economic benefits. The proposed method integrates the plan with static analysis, improving the quality of geomaterial information used in pile resistance prediction. The effectiveness of the method was demonstrated through load tests, reducing prediction errors and increasing resistance factors for redundant piles, resulting in significant cost savings.