Zhaokang Qiu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhaokang Qiu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Dongdong Chen
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
1 shared publication - Shengwu Tu
State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China
1 shared publication - Xueqi Zhao
School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
1 shared publication - Yang Liu
Affiliation School of Resources and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, China
1 shared publication - Yi Zhang
School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430023, China
1 shared publication
Latest Publications
- Open Access
Water Content Detection of Red Sandstone Based on Shock Acoustic Sensing and Convolutional Neural Network
In response to the challenge of changes in the physical and mechanical properties of red sandstone when it comes into contact with water during construction projects, this paper proposes a moisture content detection method for red sandstone based on the knocking method. Taking red sandstone as the research object, this study explores a moisture content detection approach by combining the knocking method with Convolutional Neural Network and Support Vector Machine algorithms (CNN-SVM). Specifically, this research involves knocking the surface of red sandstone specimens with a knocking hammer and precisely capturing the acoustic signals generated during the knocking process using a microphone. Subsequently, an effective detection of the moisture content in red sandstone is achieved through a method based on feature extraction from knocking sound signals and a Convolutional Neural Network classification model. This method is easy to operate. By utilizing modern signal processing techniques combined with the CNN-SVM model, it enables accurate identification and non-destructive testing of the moisture content in red sandstone even with small sample datasets. Mel Frequency Cepstral Coefficients (MFCCs) and Continuous Wavelet Transform (CWT) were separately used as features for detecting red sandstone specimens with different moisture contents. The detection results show that the classification accuracy of red sandstone moisture content using MFCCs as the feature reaches as high as 94.4%, significantly outperforming the classification method using CWT as the feature. This study validates the effectiveness and reliability of the proposed method, providing a novel and efficient approach for rapid and non-destructive detection of the moisture content in red sandstone.
Open AccessWater Content Detection of Red Sandstone Based on Shock Acoustic Sensing and Convolutional Neural Network
In response to the challenge of changes in the physical and mechanical properties of red sandstone when it comes into contact with water during construction projects, this paper proposes a moisture content detection method for red sandstone based on the knocking method. Taking red sandstone as the research object, this study explores a moisture content detection approach by combining the knocking method with Convolutional Neural Network and Support Vector Machine algorithms (CNN-SVM). Specifically, this research involves knocking the surface of red sandstone specimens with a knocking hammer and precisely capturing the acoustic signals generated during the knocking process using a microphone. Subsequently, an effective detection of the moisture content in red sandstone is achieved through a method based on feature extraction from knocking sound signals and a Convolutional Neural Network classification model. This method is easy to operate. By utilizing modern signal processing techniques combined with the CNN-SVM model, it enables accurate identification and non-destructive testing of the moisture content in red sandstone even with small sample datasets. Mel Frequency Cepstral Coefficients (MFCCs) and Continuous Wavelet Transform (CWT) were separately used as features for detecting red sandstone specimens with different moisture contents. The detection results show that the classification accuracy of red sandstone moisture content using MFCCs as the feature reaches as high as 94.4%, significantly outperforming the classification method using CWT as the feature. This study validates the effectiveness and reliability of the proposed method, providing a novel and efficient approach for rapid and non-destructive detection of the moisture content in red sandstone.