Open Access Open Access  Restricted Access Subscription or Fee Access

Optimization of Mortar Curing Control Using Artificial Intelligence and Ultrasonics

(*) Corresponding author

Authors' affiliations



The hardening process of mortar is a key element in construction and the quality of the structure. As the mortar solidifies and hardens, its properties and quality evolve, directly affecting the strength and durability of the built structure. In this work, a control that provides essential information on the evolution of mortar hardening over time is carried out. Ultrasonic testing provides an effective non-destructive solution for monitoring the setting and hardening of mortars by measuring its viscoelastic properties. However, this analysis carried out during the 28-day control period can lead to deterioration of the transducers and cables, affecting their performance. In this respect, our research is at the heart of current concerns about sustainable construction and technical efficiency, which implies optimization using a tool based on artificial intelligence neural networks. The aim of this work is to reduce the time and number of measurements carried out by an operator, while maintaining the quality of the mortar control by obtaining reliable data on the evolution of the reflection coefficient of ultrasonic waves. The results obtained in this work show that our neural network based regression model is both powerful and efficient. It is able to easily reproduce existing relationships in the training data and to optimize the experimental work by reducing the number of acquisitions from 828 to 19. All regressions performed by the model show high coefficients of determination, runtimes and success rates, confirming the validity of this model.
Copyright © 2023 Praise Worthy Prize - All rights reserved.


Artificial Intelligence; Ultrasonics; Non-Destructive Testing; Mortar; Neural Network; Coefficient of Determination; Reflection Coefficient; Neural Network Regression MLP

Full Text:



Alshboul, Z., Alzgool, H., Alzghool, H., Sustainable Use of Brine Water in Concrete Cement Mixes Alter Compression-Bending Strengths, (2022) International Review of Civil Engineering (IRECE), 13 (1), pp. 67-73.

Alwash, M., Alwash, J., Jassim, H., Impact of Different Curing Techniques on Some Mechanical Properties of Self-Compacting Concrete Containing Silica Fume, (2022) International Review of Civil Engineering (IRECE), 13 (3), pp. 208-215.

Maula, B., Enhancing Soil Shear Parameters with Polymer-Gypseous Composite Material Using an Integrated Approach, (2022) International Review of Civil Engineering (IRECE), 13 (5), pp. 420-428.

Chernavin, V., Benin, D., Galkina, D., Vorona-Slivinskaya, L., The Effect of the Reinforcing Agent from Construction Waste on the Mechanical Properties of Concrete, (2021) International Review of Civil Engineering (IRECE), 12 (4), pp. 264-270.

K.L. Scrivener, R.J. Kirkpatrick, and P.J. Monteiro, Advances in understanding hydration of Portland cement, Cement and Concrete Research, Vol. 78: 38-56, December 2015.

Y. El Bitouri, Rheological Behavior of Cement Paste: A Phenomenological State of the Art, Eng-Advances in Engineering, Vol. 4(Issue 3): 1891-1904, July 2021.

Z.L. Jiang, Y.J. Pan, J.F. Lu, and Y.C. Wang, Pore structure characterization of cement paste by different experimental methods and its influence on permeability evaluation, Cement and Concrete Research, Vol. 159: 106892, September 2022.

I. Pinarci, Y. Kocak, Hydration mechanisms and mechanical properties of pumice substituted cementitious binder, Construction and Building Mat., Vol. 335: 127528, June 2022.

A. Trnik, L. Scheinherrova, T. Kulovana, and R. Cerny, Simultaneous Differential Scanning Calorimetry and Thermogravimetric Analysis of Portland Cement as a Function of Age, Cement and Concrete Research, Vol. 37(1): 12, Jan. 2016.

Y.F. Houst, R.A. Martin, and J.M. Russel, Application of Fourier Transform infrared spectroscopy (FTIR) coupled with multivariate regression for calcium carbonate (CaCO3) quantification in cement, Construction and Building Materials, Vol. 313: 125413, December 2021.

D. Smyl, Electrical tomography for characterizing transport properties in cement-based materials: A review, Construction and Building Materials, Vol. 244(Issue 7): 118299, May 2020.

H. Lotfi, H. Banouni, B. Faiz, and H. Mesbah, Spectral analysis of ultrasonic signals backscartted by mortar: Effect of sand size and temperatures, Materials Letters: X, Vol. 15(Issue 6): 100158, September 2022.

N. Khatib, E. Ouacha, B. Faiz, M. Ezzaidi, and H. Banouni, Analysis of the attenuative behaviour of accelerated cement based materials through a series of ultrasound Pulse Echo measurements, Engineering Solid Mechanics, Vol. 7 (Issue 2): 109-120, April 2019.

P.K. Mehta, P.J. Monteiro, Concrete: Microstructure, Properties, and Materials (McGraw-Hill Education, 2014).

N.T. Seghir, O. Benaimeche, K. Krzywinski, and L. Sadowski, Ultrasonic Evaluation of Cement-Based Building Materials Modified Using Marble Powder Sourced from Industrial Wastes, Buildings, Vol. 10 (Issue 3): 38, February 2020.

C.L. Chen, A. Mahjoubfar, L.C. Tai, I.K. Blaby, A. Huang, K.R. Niazi, and B. Jalali, Deep learning in label-free cell classification, Scientific Reports, Vol. 6(Issue 1): 21471, March 2016.

M. Dordevic, The CMS Particle Flow Algorithm, The European Physical Journal Conferences, Vol. 191: pp. 02016, Valday, Russian Federation, June 2018.

H. Lotfi, D. Izbaim, H. Bitta, H. Mesbah, and H. Banouni, Assessment of ultrasonic data of signals backscattered by mortar using the principal component analysis, Data in Brief, Vol. 34(Issue 4): 106741, February 2021.

A. Janczak, Identification of Nonlinear Systems Using Neural Networks and Polynomial Models (Springer; 2005).

A. Apicella, F. Isgro, A. Pollastro, and R. Prevete, Adaptive filters in Graph Convolutional Neural Networks, Pattern Recognition, Vol. 144: 109867, December 2023.

Z. Xu, Z. Chen, W. Yi, Q. Gui, W. Hou, and M. Ding, Deep gradient prior network for DEM super-resolution: Transfer learning from image to DEM, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 150: 80-90, April 2019.

Q. Cui, L. Li, J. Cao, Stability of inertial delayed neural networks with stochastic delayed impulses via matrix measure method, Neurocomputing, Vol. 471(Issue 7): 70-78, January 2022.

Habib, T., Abouhogail, R., Modelling of Spacecraft Orbit via Neural Networks, (2021) International Review of Aerospace Engineering (IREASE), 14 (5), pp. 285-293.

Habib, T., Abouhogail, R., Efficient Simultaneous Spacecraft Attitude and Orbit Estimation via Neural Networks, (2021) International Review of Aerospace Engineering (IREASE), 14 (6), pp. 346-353.

Varsha, P., Hari, V., Intelligent Deep Learning Based Speech Receivers, (2023) International Journal on Communications Antenna and Propagation (IRECAP), 13 (4), pp. 189-196.

Gorshkova, K., Zueva, V., Kuznetsova, M., Tugashova, L., Optimizing Deep Learning Methods in Neural Network Architectures, (2021) International Review of Automatic Control (IREACO), 14 (2), pp. 93-101.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize