Improvement in the information-oriented construction of temporary soil-retaining walls using sparse modeling

This fundamental study investigates how “super-resolution” technology based on sparse modeling, which has attracted attention in various fields, can be applied to the information-oriented construction of temporary soil-retaining walls. The machine learning process adopted here is based on the analytical results of numerical computations that involve many preliminary assumptions related to soil-retaining walls, rather than the collection of images utilized in the image reconstruction technology. Consequently, bases for vectors related to the displacement of retaining walls are generated using efficient inverse analysis and “super-resolution” processing from sparse amounts of physical observation data. The purpose is to improve the properties of the inverse problem by artificial interpolation based on numerical analysis. It has been shown that the inverse analysis related to the displacement of retaining walls can be performed efficiently and that highly accurate predictions can be achieved even with limited physical observations. In general, the inverse analysis of retaining walls is an ill-posed problem. However, if the number of apparent observations reconverted by “super-resolution” technology exceeds the number of unknown parameters, then the displacement distribution of a retaining wall can be estimated efficiently. Another original idea is to break down the inverse problem into two separate problems by addressing the earth pressure distribution acting on the retaining wall. This makes it possible to identify the part to which the nonlinear inverse problem can be applied and to facilitate the efficient estimation and interpretation of the results.