A non-destructive method of the evaluation of the moisture in saline brick walls using artificial neural networks

• Autorzy: Goetzke-Pala A. , Hoła A. , Sadowski Ł.

Identyfikatory

DOI

10.1016/j.acme.2018.07.004

• Języki publikacji: EN

Abstrakty

EN

The article presents a method of non-destructive evaluation of the moisture content in saline brick walls. This method is based on the use of artificial neural networks (ANNs) that are trained and tested on a database that was built for this purpose. The database was created based on laboratory tests of sample brick walls. The database contains over 1100 sets of results. Each set consists of two parameters that describe the dampness of the tested sample walls, which were determined using dielectric and microwave methods, and also three parameters that describe the concentration of salts in these walls. The ANN with back propagation error and the Broyden–Fletcher–Goldfarb–Shanno learning algorithm (BFGS) was used. It was shown that the proposed method of assessment allows reliable results to be obtained, which was confirmed by the high values of the linear correlation coefficient for learning, testing and experimental validation.

Słowa kluczowe

EN

masonry; moisture content; non-destructive methods; artificial neural networks

PL

kamieniarstwo; sztuczne sieci neuronowe; zawartość wilgoci

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2018
• Tom: Vol. 18, no. 4
• Strony: 1729--1742
• Opis fizyczny: Bibliogr. 38 poz., fot., rys., tab., wykr.

Twórcy

autor

Goetzke-Pala A.

• Wroclaw University of Science and Technology, Faculty of Civil Engineering, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

autor

Hoła A.

• Wroclaw University of Science and Technology, Faculty of Civil Engineering, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

autor

Sadowski Ł.

• Wroclaw University of Science and Technology, Faculty of Civil Engineering, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

Bibliografia

• [1] E. Franzoni, Rising damp removal from historical masonries: a still open challenge, Constr. Build. Mater. 54 (2014) 123–136.
• [2] N. Karagiannis, M. Karoglou, A. Bakolas, M. Krokida, A. Moropoulou, Drying kinetics of building materials capillary moisture, Constr. Build. Mater. 137 (2017) 441–449.
• [3] E. Franzoni, F. Sandrolini, S. Bandini, An experimental fixture for continuous monitoring of electrical effects in moist masonry walls, Constr. Build. Mater. 25 (4) (2011) 2023–2029.
• [4] A. Hoła, Tests of the damp timber-framed construction of a historic church building, Int. Rev. Civil Eng. 6 (2) (2015) 39–42.
• [5] A. Hoła, J. Hoła, Z. Matkowski, Analysis of the moisture content of masonry walls in historical buildings using the basement of a medieval town hall as an example, Procedia Eng. 172 (2017) 363–368.
• [6] R. Mors, H. Jonkers, Effect on concrete surface water absorption upon addition of lactate derived agent, Coatings 7 (4) (2017) 51.
• [7] M.S. Camino, F.J. León, A. Llorente, J.M. Olivar, Evaluation of the behavior of brick tile masonry and mortar due to capillary rise of moisture, Mater. Constr. 64 (314) (2014) 020.
• [8] R.M. Espinosa, L. Franke, G. Deckelmann, Phase changes of salts in porous materials: crystallization, hydration and deliquescence, Constr. Build. Mater. 22 (8) (2008) 1758–1773.
• [9] C. Gentilini, E. Franzoni, S. Bandini, L. Nobile, Effect of salt crystallisation on the shear behaviour of masonry walls: an experimental study, Constr. Build. Mater. 37 (2012) 181–189.
• [10] A. Goetzke-Pala, J. Hoła, Influence of burnt clay brick salinity on moisture content evaluated by non-destructive electric methods, Arch. Civil Mech. Eng. 16 (2016) 101–111.
• [11] CSN P 73 0610: waterproofing of buildings – the rehabilitation of damp masonry and additional protection of buildings against ground moisture and against atmospheric water – the basic provision 2000.
• [12] WTA 2-6-99-D, Erganzungen zum Merkblatt 2-2-91-D Sanierputzsysteme.
• [13] R. Wójcik, Horizontal damp-proof courses in masonry – the main problem of permanent draining of buildings, Izolacje 7/8 (2012) (in Polish).
• [14] E. Franzoni, S. Bandini, Spontaneous electrical effects In masonry affected by capillary water rise: The role of salts, Constr. Build. Mater. 35 (2012) 642–646.
• [15] R. Walker, S. Pavía, M. Dalton, Measurement of moisture content in solid brick walls using timber dowel, Mater. Struct. 49 (7) (2016) 2549–2561.
• [16] Mess-U. Gann, Regeltechnik GmbH: electronic moisture meters, Catalouge 11 (2014).
• [17] A. Göller, A. Handro, J. Landgraf, A new microwave metod for moisture measurement in building materials,Athens, GA, USA, Proceedings of the 3rd Workshop on Electromagnetic Wave Interaction with Water and Moisture Substances 1999.
• [18] A. Hoła, Measuring of the moisture content in brick walls of historical buildings – the overview of methods,Riga Technical University, Riga, Latvia, 27–29 September, 3rd International Conference ‘‘Innovative Materials, Structures and Technologies’’ 2017.
• [19] G.P. Cetrangolo, L.D. Domenech, G. Moltini, A.A. Morquio, determination of moisture content in ceramic brick walls using ground penetration radar, J. Nondestruct. Eval. 36 (1) (2017) 12.
• [20] I.V. Miller, E. Albert, R. Spragg, F.C. Antico, W. Ashraf, et al., Determining the moisture content of pre-wetted lightweight aggregate: assessing the variability of the paper towel and centrifuge methods,Purdue University, West Lafayette, IN, USA, 24–26 July, 4th International Conference on the Durability of Concrete Structures 2014.
• [21] A. Goetzke-Pala, Identification of the moisture content In brick walls on the basis of non-destructive testing and also with the use of artificial neural networks (Ph.D. thesis), Faculty of Civil Engineering, Wrocław University of Science and Technology, Wrocław, 2016 (in Polish).
• [22] H. Nowak, Application of thermal research in construction, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław, 2012 (in Polish).
• [23] J. Hoła, Z. Matkowski, K. Schabowicz, J. Sikora, K. Nita, S. Wójtowicz, Identification of moisture content in brick walls by means of impedance tomography, Compel 31 (2012) 1774–1792.
• [24] C. Nunes, L. Pel, J. Kunecký, Z. Slížková, The influence of the pore structure on the moisture transport in lime plaster-brick systems as studied by NMR, Constr. Build. Mater. 142 (2017) 395–409.
• [25] A. Fuchs, M.J. Moser, H. Zangl, T. Bretterklieber, Using capacitive sensing to determine the moisture content of wood pellets – investigations and application, Int. J. Smart Sens. Intell. Syst. 2 (4) (2009) 293–308.
• [26] W. Leschnik, Feuchtemessung an Baustoffen–Zwischen Klassik und Moderne, Deutsche Gesellschaft für Zerstörungsfreie Prüfung, Berlin, Berichtsband, 1999.
• [27] D. Funk, P. Meszaros, Z. Gillay, J.H. Rampton, L.D. Freese, New dielectric moisture measurement technologies, in: International Quality Grains Conference Proceedings, 2004.
• [28] A. Goetzke-Pala, J. Hoła, Research on the moisture content of salted ceramic bricks by non-destructive method, Mater. Bud. 7 (2013) 85–87 (in Polish).
• [29] S. Shapiro, M. Wilk, An analysis of variance test for normality, Biometrika 52 (1965) 591–611.
• [30] H. Levene, Robust tests for equality of variances, in: Ingram Olkin; Harold Hotelling; et al. Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, Stanford University Press, 1960, pp. 278–292.
• [31] M.H. Rafiei, W.H. Khushefati, R. Demirboga, H. Adeli, Neural network, machine learning, and evolutionary approaches for concrete material characterization, ACI Mater. J. 113 (6) (2016).
• [32] X. Li, J. Qiu, Q. Shang, F. Li, Simulation of reservoir sediment flushing of the three gorges reservoir using an artificial neural network, Appl. Sci. 6 (5) (2016) 148.
• [33] Z.H. Duan, S.C. Kou, C.S. Poon, Prediction of compressive strength of recycled aggregate concrete using artificial neural networks, Constr. Build. Mater. 40 (2013) 1200–1206.
• [34] E.M. Golafshani, A. Behnood, Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete, J. Clean. Prod. 176 (2017) 1163–1176.
• [35] P.G. Asteris, S. Nozhati, M. Nikoo, L. Cavaleri, M. Nikoo, Krill herd algorithm-based neural network in structural seismic reliability evaluation, Mech. Adv. Mater. Struct. (2018), http://dx.doi.org/10.1080/15376494.2018.1430874.
• [36] C. Qi, A. Fourie, Q. Chen, Q. Zhang, A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill, J. Clean. Prod. 183 (2018) 566–578.
• [37] C.G. Broyden, The convergence of a class of double-rank minimization algorithms, J. Inst. Math. Appl. 6 (1970) 76–90.
• [38] A. Goetzke-Pala, J. Hoła, Ł. Sadowski, Non-destructive neural identification of the moisture content of saline ceramic bricks, Constr. Build. Mater. 113 (2016) 144–152.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)