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    柯栋方, 辛振波, 张厚江, 彭林. 基于敲击声MFSC特征CNN模型的古建筑木材物理力学性能评估[J]. 北京林业大学学报, 2023, 45(2): 149-160. DOI: 10.12171/j.1000-1522.20220364
    引用本文: 柯栋方, 辛振波, 张厚江, 彭林. 基于敲击声MFSC特征CNN模型的古建筑木材物理力学性能评估[J]. 北京林业大学学报, 2023, 45(2): 149-160. DOI: 10.12171/j.1000-1522.20220364
    Ke Dongfang, Xin Zhenbo, Zhang Houjiang, Peng Lin. Evaluation of physical and mechanical properties of ancient building wood based on MFSC characteristic CNN model of knocking sound[J]. Journal of Beijing Forestry University, 2023, 45(2): 149-160. DOI: 10.12171/j.1000-1522.20220364
    Citation: Ke Dongfang, Xin Zhenbo, Zhang Houjiang, Peng Lin. Evaluation of physical and mechanical properties of ancient building wood based on MFSC characteristic CNN model of knocking sound[J]. Journal of Beijing Forestry University, 2023, 45(2): 149-160. DOI: 10.12171/j.1000-1522.20220364

    基于敲击声MFSC特征CNN模型的古建筑木材物理力学性能评估

    Evaluation of physical and mechanical properties of ancient building wood based on MFSC characteristic CNN model of knocking sound

    • 摘要:
        目的  我国有大量的木结构古建筑,在现场对古建筑木构件正常木材的物理力学性能给予方便的检测评估,是古建筑木结构日常保护、修缮和安全评估的刚性需求。本研究对敲击声信号引入机器学习算法处理,力图将便捷的敲击方式应用于古建筑木材物理力学性能的无损检测。
        方法  以北京某皇家古建筑拆修下来的4段落叶松旧木构件为原材料,加工无疵试件,首先探究木试件尺寸、密度对敲击声信号的影响,试验测定木试件的密度、抗弯强度、抗弯弹性模量、顺纹抗压强度等物理力学性能参数;然后对试验采集的敲击声信号进行梅尔频率谱系数(MFSC)特征提取,以敲击声MFSC特征为输入、试件物理力学性能为输出,构建古建筑木材物理力学性能卷积神经网络(CNN)评估模型。
        结果  试件尺寸对敲击声信号没有影响,密度较高试件的敲击声信号的主峰频率较高;失活层对模型性能有较为明显的影响,失活层失活率为0.2时的拟合效果最佳;所建立的模型对古建筑木材物理力学性能的评估效果良好,密度、抗弯强度、抗弯弹性模量、顺纹抗压强度评估值与真实值之间的决定系数分别达到0.873、0.819、0.746、0.860。
        结论  本研究构建的基于敲击声MFSC特征CNN模型,对古建筑木材物理力学性能进行检测评估是可行的。

       

      Abstract:
        Objective  There are a large number of ancient buildings with wooden structures in China. How to conveniently test and evaluate the physical and mechanical properties of ancient building normal timber is a rigid requirement for the daily protection, repair and safety assessment of ancient wood building structures. In this study, the machine learning algorithm was introduced to the knocking sound signal, and try to apply the convenient knocking method to the nondestructive testing of the physical and mechanical properties of ancient building wood.
        Method  The four-section ancient larch (Larix gmelinii) timber members dismantled from an ancient royal building in Beijing were used as raw materials to process clear specimens. Firstly, the influence of size and density of the wood specimen on the knocking sound signal was investigated. And the physical and mechanical property parameters such as density, modulus of rupture, modulus of elasticity and compressive strength parallel to grain of wood specimens were measured experimentally. Then, the Mel frequency spectral coefficient (MFSC) feature extraction was carried out on the knocking sound signal collected in the experiment. Taking the knocking sound MFSC feature as the input, and the physical and mechanical properties of the specimen as the output, a convolutional neural network (CNN) evaluating model for the physical and mechanical properties of ancient building timber member was constructed.
        Result  The size of the specimen had no effect on the knocking signal, and the dominant peak frequency of the knocking signal of the specimen with higher density was higher. The dropout layer had a significant impact on the performance of the model, and the fitting effect was the best when the dropout rate was 0.2. The established model had a good effect on the evaluation of physical and mechanical properties of ancient building wood, and the coefficient of determination between the evaluation value of density, modulus of rupture, modulus of elasticity and compressive strength parallel to grain and the real value reached 0.873, 0.819, 0.746 and 0.860, respectively.
        Conclusion  The CNN model constructed in this study based on the MFSC feature of knocking sound is feasible to detect and evaluate the physical and mechanical properties of timber in ancient buildings.

       

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