New insight into the prediction of strength properties of cementitious mortar containing nano‐ and micro‐silica based on porosity using hybrid artificial intelligence techniques

Abstract

Nowadays, the accurate prediction of strength properties of cementitious materials containing nano‐ and micro‐silica (NS–MS) remains an open question because of the highly nonlinear function of its constituents on the porosity. In the present study, a combined framework is developed by integrating ant colony optimization (ACO), particle swarm optimization (PSO), and biogeography‐based optimization (BBO) with the artificial neural network (ANN) to predict compressive and flexural strengths of cement mortar in two different forms of ignoring (ANNII) and considering (ANNIII) the porosity as an input parameter. This procedure is accomplished considering the porosity effect on the strengths and implementing an experimental program containing 32 mixes (960 specimens) with different NS–MS contents at various ages. Macro‐ and micro‐structural analyses showed that NS–MS caused more decreased pore structure, and thus this situation increases strength properties compared to their separate use. Also, MBBO‐MOANNIII results indicated an improvement in convergence speed and model accuracy compared to other models. This improvement is because of considering the porosity.

Recent developments in utilization of nano silica in self‐compacting concrete: A review

“…One is that the high pozzolanic activity of NS can promote hydration, the reaction between SiO 2 and cement hydration product Ca(OH) 2 produces C-S-H gels, subsequently the generated C-S-H gels can fill the pores in SCC. 6,29,31,64,84,86,87 Furthermore, nano-particles have great surface energy, and nano-particles are closely combined with cement hydration products, which can promote C-S-H gels to adhere to the NS particles, making the structure of C-S-H gels more stable. 88,89 For instance, after 28-days of curing, compared with the control group (see Figure 10a), the C-S-H gels of SCC containing 3% NS are more evenly distributed (see Figure 10b), and no obvious pores are observed.…”

“…30,32,42,48,82 On the other hand, it is not recommended to reduce the negative impact of NS on the workability of SCC by increasing the water/binder ratio, as this will increase the voids developed within the SCC, weakening its microstructure. 83,84 For example, the study by Hani et al 4 showed that when NS content was 0.75%, as the water/ binder ratio was increased from 0.41 to 0.5, the 28-days CS of the SCC decreased by about 13%.…”

“…As a SCM for SCC, NS usually plays two roles. One is that the high pozzolanic activity of NS can promote hydration, the reaction between and cement hydration product Ca(OH) 2 produces C‐S‐H gels, subsequently the generated C‐S‐H gels can fill the pores in SCC 6,29,31,64,84,86,87 . Furthermore, nano‐particles have great surface energy, and nano‐particles are closely combined with cement hydration products, which can promote C‐S‐H gels to adhere to the NS particles, making the structure of C‐S‐H gels more stable 88,89 .…”

Smart cement‐sensor composite: The evolution of nanomaterial in developing sensor for structural integrity

“…On one hand, it is observed that image processing, artificial, and machine learning techniques are suitable for identifying the failures and long strength properties of structures. [228][229][230][231] But on the other hand, using nano fillers for the development of SSCM with high piezoresistive characteristics and self-sensing functionalities could possibly contributes to SHM. 232 This review discusses the multiple processes and different experiments to develop smart cement-sensor composite using the best nano material at the optimum proportion to be embedded in the structural elements, 111154 such as beam, column, and beam-column joints to determine the stress/ strain, crack, and damage under different loading conditions.…”

“…Visualizing, identifying and monitoring the failure patterns and cracks in the concrete structures are challenging tasks. On one hand, it is observed that image processing, artificial, and machine learning techniques are suitable for identifying the failures and long strength properties of structures 228–231 . But on the other hand, using nano fillers for the development of SSCM with high piezoresistive characteristics and self‐sensing functionalities could possibly contributes to SHM 232 .…”