Efficient value of information analysis for optimal monitoring placement of reinforced slopes by collaborative reliability updating

Determining optimal monitoring placement (e.g., number and locations of monitoring variables) of engineering structures (e.g., reinforced slopes) is essential for field instrumentation and subsequent reliability assessments. Value of information (VoI) provides a decision-theoretic metric for effective structure monitoring planning by quantifying potential benefits of various monitoring schemes (e.g., possible combinations of monitoring candidates). However, computing VoI often involves a significant number of reliability analyses corresponding to possible monitoring outcomes (PMOs) from different monitoring schemes, which is computationally challenging. The challenge becomes more profound when reliability updating is conducted using a physics-based model (e.g., finite element model) and the failure probability is rare. This study proposes a collaborative reliability updating approach for VoI-based optimal monitoring placement of reinforced slopes. The proposed approach first decomposes the reliability updating problem into three reliability analysis problems, and then generates candidate sample pools that will be employed, collaboratively, for repeated estimations of the three decomposed reliability problems considering PMOs from different monitoring schemes. Using the proposed approach, a huge number (e.g., over a million) of reliability analyses can be accomplished in a cost-effective way. A simple numerical example and a geotechnical reinforced slope are adopted to illustrate the proposed approach for optimizing monitoring configurations considering different monitoring schemes.