Combining non-invasive techniques for reliable prediction of soft stone strength in historic masonries
| Autor: | Emilia Vasanelli, Denys Breysse, Zoubir Mehdi Sbartaï, Donato Colangiuli, Angela Calia |
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| Rok vydání: | 2017 |
| Předmět: |
Engineering
0211 other engineering and technologies 020101 civil engineering 02 engineering and technology Uniaxial compressive strength assessment 0201 civil engineering law.invention law Destructive testing 021105 building & construction Linear regression General Materials Science Geotechnical engineering Hammer Artificial Neural Networks Reliability (statistics) Civil and Structural Engineering Limestone masonry Artificial neural network business.industry Non-destructive tests Non invasive Regression analysis Building and Construction Structural engineering Combination techniques Compressive strength business Cross-validation procedure |
| Zdroj: | Construction & building materials 146 (2017): 744–754. doi:10.1016/j.conbuildmat.2017.04.146 info:cnr-pdr/source/autori:Vasanelli, Emilia; Colangiuli, Donato; Calia, Angela; Sbartaï, Zoubir Mehdi; Breysse, Denys/titolo:Combining non-invasive techniques for reliable prediction of soft stone strength in historic masonries/doi:10.1016%2Fj.conbuildmat.2017.04.146/rivista:Construction & building materials/anno:2017/pagina_da:744/pagina_a:754/intervallo_pagine:744–754/volume:146 |
| ISSN: | 0950-0618 |
| Popis: | In this study, some NDTs (Ultrasonic Pulse Velocity UPV and Rebound Hammer) and uniaxial compressive test on microcores (UCS m ) as a moderately destructive test, were investigated as tools for assessing the uniaxial compressive strength (UCS) of a soft limestone. Correlations between UCS and results of each above-mentioned tests were determined by a univariable regression analysis. Artificial Neural Network and the Multiple Regression Analyses were considered to search correlations between UCS and combined results of the non-invasive tests. An iterative cross-validation procedure was implemented to validate the predictive performances of the models. It was found that combining UPV and UCS m results gives the best reliability in the indirect estimation of UCS, with a notably reduced predictive error. |
| Databáze: | OpenAIRE |
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