Historical buildings, such as museums, are an important class of buildings because ancient historical artefacts are collected and preserved in them. These buildings must be maintained to prolong their service life and monitored continuously for possible signs of building damages. Determining the compressive strength is essential for predicting the remaining service life of museums because monumental museum buildings usually comprise load-bearing masonry walls. This study analysed the applicability of three machine learning techniques as alternative methods for predicting the compressive strength of masonry constructions. A database was established by performing experimental testing on 44 masonry specimens. In the nondestructive techniques used, the ultrasonic pulse velocity and the rebound number were inputs and the compressive strength of masonry unit (\(f_{\text {mu}}\)) was the desired output. The remaining compressive strength of a museum was determined using commonly available nondestructive testing apparatus. The model predictions obtained through nondestructive measurements were compared with the experimental results obtained through the destructive testing of masonry units in the laboratory. The proposed approach was compared with traditional empirical models and commonly used machine learning techniques. The analyses revealed that machine learning techniques are robust, can successfully be used for the prediction of the remaining compressive strength of historical constructions, and thus can provide decision support for inspection professionals.

中文翻译：

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使用通过无损测试在博物馆案例研究中验证的机器学习技术预测未增强砖砌体的抗压强度

历史建筑物，例如博物馆，是一类重要的建筑物，因为古代的历史文物被收集并保存在其中。必须维护这些建筑物以延长其使用寿命，并不断对其进行监视，以防建筑物损坏。确定抗压强度对于预测博物馆的剩余使用寿命至关重要，因为纪念性的博物馆建筑通常包含承重砌体墙。这项研究分析了三种机器学习技术作为预测砌体建筑抗压强度的替代方法的适用性。通过对44个砖石标本进行实验测试，建立了一个数据库。在非破坏性技术中，\（f _ {\ text {mu}} \））是所需的输出。博物馆的剩余抗压强度是使用常用的无损检测设备确定的。将通过非破坏性测量获得的模型预测与通过在实验室中对砌体单元进行破坏性测试获得的实验结果进行比较。将该方法与传统的经验模型和常用的机器学习技术进行了比较。分析表明，机器学习技术是可靠的，可以成功地用于预测历史构造的剩余抗压强度，从而可以为检验专业人员提供决策支持。