Long-term damage of historic masonry: A probabilistic model

Anzani, Anna; Garavaglia, Elsa; Binda, L.

doi:10.1016/j.conbuildmat.2008.02.010

Cited by 35 publications

(19 citation statements)

References 18 publications

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“…In a typical - curve of a cyclic fatigue test (figure 4) it is possible to identify three distinct stages: stage I, where deformations are seen to increase rapidly (accounting for ca 10% of the service life of test piece); stage II, of stabilisation, where the deformations increase gradually at a virtually constant stress (10-80% of test piece life); stage III, with a rapid increase till failure [4]. Various authors [5][6] have shown that fatigue life of a material subjected to cyclic loading tests is strictly correlated to the evolution of the deformations during stage II.…”

Section: Results Of the Experimental Campaignmentioning

confidence: 99%

Fatigue behaviour analysis for the durability prequalification of strengthening mortars

Bocca

Grazzini

Masera

2011

J. Phys.: Conf. Ser.

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Abstract. An innovative laboratory procedure used as a preliminary design stage for the prequalification of strengthening mortars applied to historical masonry buildings is described. In the analysis of the behaviour of masonry structures and their constituent materials, increasing importance has been assumed by the study of the long-term evolution of deformation and mechanical characteristics, which may be affected by both loading and environmental conditions. Through static and fatigue tests on mixed specimens historical brick-reinforced mortar it has been possible to investigate the durability of strengthening materials, in order to select, from a range of alternatives, the most suitable for the historical masonry. Cyclic fatigue stress has been applied to accelerate the static creep and to forecast the corresponding creep behaviour of the historical brick-strengthening mortar system under static long-time loading. This methodology has proved useful in avoiding the errors associated with materials that are not mechanically compatible and guarantees the durability of strengthening work. The experimental procedure has been used effectively in the biggest restoration building site in Europe, the Royal Palace of Venaria, and it is in progress of carrying out at the Special Natural Reserve of the Sacro Monte di Varallo, in Piedmont (Italy).

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Section: Results Of the Experimental Campaignmentioning

confidence: 99%

Fatigue behaviour analysis for the durability prequalification of strengthening mortars

Bocca

Grazzini

Masera

2011

J. Phys.: Conf. Ser.

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“…The collapses of the Bell Tower in Venice in 1902 and the civic Tower of Pavia in 1989 were both attributed to local overstressing of structural components as a result of combined creep and microcracking effects [2,6]. Recently, Anzani et al [4] introduced successful probabilistic approach for the estimate of the residual life of the material and provided knowledge of the primary, secondary and tertiary creep phases. However, this approach required prior knowledge about the identification of significant thresholds of critical strain, considering the slope of the strain versus time diagram.…”

Section: Creep Of Brickworkmentioning

confidence: 99%

Neural network modeling of time-dependent creep deformations in masonry structures

El-Shafie

Abdelazim

Noureldin

2009

Neural Comput & Applic

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Stresses and deformations in concrete and masonry structures can be significantly altered by creep. Thus, neglecting creep could result in un-conservative design of new structures and/or underestimation of the level of its effect on stress redistribution in existing structures. Brickwork has substantial creep strain that is difficult to predict because of its dependence on many uncontrolled variables. Reliable and accurate prediction models for the long-term, time-dependent creep deformation of brickwork structures are needed. Artificial intelligence techniques are suitable for such applications. A model based on radial basis function neural networks (RBFNN) is proposed for predicting creep and is compared to a multi-layer perceptron neural network (MLPNN) model recently developed for the same purpose. Accurate prediction of creep was achieved due to the simple architecture and fast training procedure of RBFNN model especially when compared to MLPNN model. The RBFNN model shows good agreement with experimental creep data from brickwork assemblages collected over the last 15 years. Abbreviations AI Artificial intelligence ANNArtificial neural networks Bias Jack (PE J ) Jackknife bias for the prediction error of the creep compliance dDistance from the center of the radial basis function (a measure of the Gaussian curve spread) E(t 0 ) Material modulus of elasticity at time of load application t 0 J(t, t 0 ) Creep compliance between time t 0 and t N Number of basis functions n Total number of observations in the compliance matrix PE J Prediction error in creep compliance PE Ài J Prediction error calculated for the Jackknife reduced compliance matrix J x Experimentally determined creep compliance J p Predicted creep compliance RH Relative humidity RBFNN Radial basis function neural networks SE Jack (PE J ) Jackknife standard error for the prediction error of the creep compliance t Time of creep prediction t 0 Time of load application W 0Bias parameter W j e cr ðt; t 0 Þ Creep strain between time t 0 and t e(t 0 ) Instantaneous strain at time t 0 u Radial basis function /(t, t 0 ) Creep coefficient between time t 0 and t k(t 0 )

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“…Non-destructive techniques can be used for the following purposes: detection of hidden structural elements; cataloguing of masonry and masonry materials, mapping of heterogeneities of materials [2]; evaluation of the extent of mechanical damage in cracked structures; detection of voids and flaws [3]; evaluation of moisture content and capillary rise; detection of surfaced decay; evaluation of mortar, brick and stone mechanical and physical properties [4]; masonry durability problems [5]; and masonry creep (long-term) damage [6]. Up to now, most of the non-destructive techniques applied to historical structures are based in wave propagation and temperature detection [7], generally the same applied in concrete.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach for NDT data fusion: The Saint Torcato church case study

Ramos

Miranda

Mishra

et al. 2015

Engineering Structures

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This paper presents a methodology based on the Bayesian data fusion techniques applied to non-destructive and destructive tests for the structural assessment of historical constructions. The aim of the methodology is to reduce the uncertainties of the parameter estimation. The Young's modulus of granite stones was chosen as an example for the present paper. The methodology considers several levels of uncertainty since the parameters of interest are considered random variables with random moments. A new concept of Trust Factor was introduced to affect the uncertainty related to each test results, translated by their standard deviation, depending on the higher or lower reliability of each test to predict a certain parameter.

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Long-term damage of historic masonry: A probabilistic model

Cited by 35 publications

References 18 publications

Fatigue behaviour analysis for the durability prequalification of strengthening mortars

Fatigue behaviour analysis for the durability prequalification of strengthening mortars

Neural network modeling of time-dependent creep deformations in masonry structures

A Bayesian approach for NDT data fusion: The Saint Torcato church case study

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