This experimental study presents the effect of different surfactants on micro-scale carbon fiber (CFs) distribution into carbon fiber reinforced cement-based composites (CFRC) in terms of flexural and compressive strength, stiffness, flexural toughness, and strain-sensing ability. Conducting a narrative review of the literature focusing on the fibers’ separation, this paper follows a methodology introducing a combination of mechanical and chemical carbon fibers dispersion, as well as the different mixing processes (wet or dry). Three types of surfactants: Carboxymethyl cellulose (CMC), cellulose nanocrystal (CNC), and superplasticizer (SP), were applied to evaluate the CFs distribution in the cement paste matrix. Compressive and flexural strength, modulus of elasticity, and ductility of the cement-based composites (CFRC) reinforced with 0.5 wt.% CFs were investigated by three-point bending and compressive tests; flexure tests were also conducted on notched 20 × 20 × 80 mm specimens using the Linear Elastic Fracture Mechanics (L.E.F.M.) theory. Moreover, the electrical conductivity and the piezoresistive response were determined by conducting electrical resistance measurements and applying compressive loading simultaneously. The results clearly reveal that the CFs/SP solution or the CFs’ dry incorporation led to a significant enhancement of flexural strength by 32% and 23.7%, modulus of elasticity by 30% and 20%, and stress-sensing ability by 20.2% and 18.2%, respectively. Although the wet mixing method exhibits improved mechanical and electrical conductivity performance, constituting an adequate strain and crack sensor, the authors propose dry mixing as the most economical method, in addition to the enhanced mechanical and electrical responses. The authors recommend an effective method for structural health monitoring systems combining an economical CFs insertion in cementitious smart sensors with great mechanical and self-sensing responses.

The current paper presents the results of an experimental study of carbon nano-, micro-, and hybrid-modified cementitious mortar to evaluate mechanical performance, energy absorption, electrical conductivity, and piezoresistive sensibility. Three amounts of single-walled carbon nanotubes (SWCNTs), namely 0.05 wt.%, 0.1 wt.%, 0.2 wt.%, and 0.3 wt.% of the cement mass, were used to prepare nano-modified cement-based specimens. In the microscale modification, 0.05 wt.%, 0.5 wt.%, 1.0 wt.% carbon fibers (CFs) were incorporated in the matrix. The hybrid-modified cementitious specimens were enhanced by adding optimized amounts of CFs and SWCNTs. The smartness of modified mortars, indicated by their piezoresistive behavior, was investigated by measuring the changes in electrical resistivity. The effective parameters that enhance the composites’ mechanical and electrical performance are the different concentrations of reinforcement and the synergistic effect between the types of reinforcement used in the hybrid structure. Results reveal that all the strengthening types improved flexural strength, toughness, and electrical conductivity by about an order of magnitude compared to the reference specimens. Specifically, the hybrid-modified mortars presented a marginal reduction of 1.5% in compressive strength and an increase in flexural strength of 21%. The hybrid-modified mortar absorbed the most energy, 1509%, 921%, and 544% more than the reference mortar, nano-modified mortar, and micro-modified mortar, respectively. The change rate of impedance, capacitance, and resistivity in piezoresistive 28-day hybrid mortars improved the tree ratios by 289%, 324%, and 576%, respectively, for nano-modified mortars and by 64%, 93%, and 234%, respectively, for micro-modified mortars.

In this paper, the prediction of compressive cement strength using the fuzzy linear regression (FLR) and adaptive neuro-fuzzy inference system (ANFIS) methods was studied. Specifically, an accurate prediction method is needed as the modeling of cement strength is a difficult task, which is based on its composite nature. However, many approaches are widely implemented in strength-predicting problems, such as the artificial neural network (ANN), Mamdani fuzzy rules in MATLAB, FLR and ANFIS models. Applying these methods and comparing the results with the corresponding observed ones, we concluded that the ANFIS method successfully decreased the level of uncertainty in predicting cement strength, as the average percentage error level was extremely low. Although the FLR method had the highest average percentage error level compared with the other methods, it provides a standard equation to estimate the output values by using symmetric triangular fuzzy numbers and determines the most important factor in increasing compressive strength, in contrast to ANFIS and ANN, which are black box models, and to the fuzzy method, which uses rules without providing the specific way by which the results come out. Thus, ANFIS and FLR are appropriate methods for dealing with engineering mathematical models by using fuzzy logic.

Computational approaches could provide a viable and cost-effective alternative to expensive experiments for accurately evaluating the nonlinear constitutive behavior of cementitious nanocomposite materials. In the present study, the mechanical properties of cement paste reinforced with multi-wall carbon nanotubes (MWCNTs) are examined experimentally and numerically. A multiscale computational approach is adopted in order to verify the experimental results. For this scope, a random sequential adsorption algorithm was developed to generate non-overlapping matrix-inclusion three-dimensional (3D) representative volume elements (RVEs), considering the inclusions as straight elements. Nonlinear finite element analyses (FEA) were performed, and the homogenized elastic and inelastic mechanical properties were computed. The use of a multiscale computational approach to accurately evaluate the nonlinear constitutive behavior of cementitious materials has rarely been explored before. For this purpose, the RVEs were analyzed both in pure tension and compression. Young’s modulus as well compressive and tensile strength results were compared and eventually matched the experimental values. Moreover, the effect of MWCNTs on the nonlinear stress–strain behavior of reinforced cement paste was noted. Subsequently, three-point bending tests were conducted, and the stress–strain behavior was verified with FEA in the macro scale. The numerical modeling reveals a positive correlation between the concentration of MWCNTs and improved mechanical properties, assuming ideal dispersion. However, it also highlights the impact of practical limitations, such as imperfect dispersion and potential defects, which can deteriorate the mechanical properties that are observed in the experimental results. Among the different cases studied, that with a 0.1 wt% MWCNTs/CP composite demonstrated the closest agreement between the numerical model and the experimental measurements. The numerical model achieved the best accuracy in estimating the Young’s modulus (underestimation of 13%), compressive strength (overestimation of 1%), and tensile strength (underestimation of 6%) compared to other cases. Overall, these numerical findings contribute significantly to understanding the mechanical behavior of the nanocomposite material and offer valuable guidance for optimizing cement-based composites for engineering applications.

Seismic analysis is recognized as the most important factor in the design of structures that ensures the protection of the building after the occurrence of an e