Strengthening historical brick masonry walls is important because these walls are major load-bearing members in many architectural heritages. However, historical brick masonry has low elastic modulus and low strength, historical masonry walls are prone to surface treatment or other structural intervention, and some of the walls lack integrity. These characteristics make effective strengthening of historical masonry walls difficult. To address the issue, strengthening layers made up of ultra-high performance concrete (UHPC) are potentially useful. To investigate the strengthening effect of the UHPC layers, the authors constructed three squat walls using historical bricks and mortar collected from the rehabilitation site of a historical building, and strengthened two of the walls with a UHPC layer and a reinforced polymer mortar layer respectively. The three walls were broken down by horizontal cyclic force along with constant vertical compression, and then the unstrengthened one was strengthened in-situ by a UHPC layer and was tested again. The experimental results indicate that the UHPC layers significantly improved the in-plane shear resistance and cracking load of the squat walls, without decreasing the walls’ ultimate deformation. They effectively strengthened both moderately and severely damaged historical masonry walls, because the UHPC filled the existing damages and improved the integrity of the masonry substrate. In addition, the UHPC layers intervened the historical walls less than the reinforced polymer mortar layer. Therefore, the UHPC layers are efficient in strengthening historical squat masonry walls.
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.
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