{'fraction_non_alphanumeric': 0.010355029585798817, 'fraction_numerical': 0.0, 'mean_word_length': 6.121052631578947, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Shear resistance estimation for unreinforced masonry walls based on Gaussian process models

Uncertainty results in misestimate of the shear resistance of unreinforced masonry walls. The misestimate is difficult to correct using deterministic calculation models even when sophisticated numerical models are used. To address this issue, the authors converted the deterministic calculation models for shear resistance of unreinforced masonry walls into prior Gaussian process models and then collected experimental results to train the prior Gaussian process models. They evaluated the resultant posterior Gaussian process models and found two of them are preferable. The two favorable posterior Gaussian process models can well interpolate the collected experimental results and can predict the shear resistance of the unreinforced masonry walls in the authors’ experiments with full information. The interpolation and predication demonstrate that the posterior Gaussian process models can combine and weight prior engineering judgments and observational information from experimental results and then can quantitatively specify the remaining uncertainty of the estimation. In addition, the Gaussian process models are efficient, favorable for reliability analysis, and easily improvable. Therefore, they are potentially useful in engineering practices.