Nondestructive detection of fiber content in steel fiber reinforced concrete through percussion method coordinated with a hybrid deep learning network

Accurate inspection of the steel fiber content is necessary to assure the designed performance and required quality of steel fiber reinforced concrete (SFRC). This paper presents a percussion-based nondestructive methodology in detecting the steel fiber content in SFRC components, incorporating a deep neural network capable of intelligent detection and automated classification of steel fiber content without the need for signal transformation or manual feature extraction. This research makes several noteworthy contributions. First of all, the percussion acoustic signals were captured on a smartphone from SFRC cubes with varying fiber volumes in a range of 0–2%. Subsequently, a hybrid deep learning framework named 1D CNN-BiLSTM, which harnesses advantages from both the one-dimensional conventional neural network (1D CNN) and the bidirectional long short-term memory (BiLSTM) network, was established and trained to classify these acoustic signals labeled with specific fiber contents. The results well manifest the excellent performances of the proposed network in classifying the steel fiber content, achieving a testing accuracy of 98%, which outperforms conventional machine learning methods, 1D CNN and 1D CNN-LSTM. Moreover, the noise resilience and adaptability of 1D CNN-BiLSTM were also greatly enhanced in contrast to these baseline methods. The testing accuracy exceeds 90.0% under strong noisy conditions in an SNR range of 2–10 dB. In addition, the trained model also presents an exceptional adaptability to identifying previously unseen signals from SFRC beams, yielding an accuracy of 90.34%. •The percussion-based method was extended to detect the steel fiber content in SFRC.•A hybrid neural network 1D CNN-BiLSTM was built for automated classifying audio signals.•The 1D CNN-BiLSTM achieved superior testing accuracy over conventional algorithms.•The noise resilience and application adaptability of the 1D CNN-BiLSTM were studied.