Study on Automatic Compaction Judgment System for Concrete Based on Deep Learning: System Proposal and Evaluation

Widely used as a construction material, concrete is composed of cement, water, sand and gravel. First, these materials are mixed, and then placing into a mold, and then filled and compacted with a vibrator. Next, the concrete is cured in a wet state. Each of these work steps is an important work that affects the quality of the concrete after hardening. However, the degree and the completion time of concrete compaction have been conventionally determined by visual judgment and feeling based on the experience of engineers. Such a judgment method has a risk of contributing to variations in the quality of concrete and deterioration of quality. Considering the situation where the number of engineers continues to decrease, there is a demand for consistent judgment indicators and technological development aimed at improving productivity. This paper describes a concrete compaction judgment system with deep learning which can be a substitute for conventional visual judgment, and proposes a practical system that can realize highly accurate judgment the concrete surface in each small area, based on the system with deep learning that the authors have developed so far. An evaluation experiments was performed to verify its performance, and its usefulness was confirmed.