Porosity- and reliability-based evaluation of concrete-face rock dam compaction quality

- A porosity- and reliability- based compaction quality evaluation method is developed.
- Data rely on the real-time monitoring system for construction quality and spot tests accumulated during construction.
- An artificial neural network based on elitist genetic algorithm for porosity prediction is established.
- Reliability can reflect variability in the compaction system and the impact of variability on compaction quality evaluation.
- The proposed method considers the material source parameters and their variation.

Compaction quality evaluation of concrete-faced rockfill dams (CFRDs) is a complex nonlinear process. Conventional evaluation methods depend on random spot tests, and porosity is considered as the main evaluation index. However, reliability of results is neglected in existing studies. Considering compaction parameters, material properties and their variability characteristics, an evaluation approach of CFRD compaction quality based on porosity and reliability is proposed. Reliability analysis is introduced to measure the variability in highly variable factors and calculate the porosity- and reliability-based index. Porosities of the entire work area are predicted using an elitist genetic algorithm-based artificial neural network, which are consistent with the measured values with an error < 5%. Data used in this study are based on the results from the real-time monitoring system for construction quality and spot tests accumulated during construction. The applicability of the proposed approach is demonstrated by an illustrative case study in China.