The use of fibers as mass reinforcement to delay cracking and to improve the strength and the post-cracking performance of reinforced concrete (RC) beams has been well documented. However, issues of common engineering practice about the beneficial effect of steel fibers to the seismic resistance of RC structural members in active earthquake zones have not yet been fully clarified. This study presents an experimental and a numerical approach to the aforementioned question. The hysteretic response of slender and deep steel fiber-reinforced concrete (SFRC) beams reinforced with steel reinforcement is investigated through tests of eleven beams subjected to reversal cyclic loading and numerical analysis using 3D finite element (FE) modeling. The experimental program includes flexural and shear-critical SFRC beams with different ratios of steel reinforcing bars (0.55% and 1.0%), closed stirrups (from 0 to 0.5%), and fibers with content from 0.5 to 3% per volume. The developed nonlinear FE numerical simulation considers well-established relationships for the compression and tensional behavior of SFRC that are based on test results. Specifically, a smeared crack model is proposed for the post-cracking behavior of SFRC under tension, which employs the fracture characteristics of the composite material using stress versus crack width curves with tension softening. Axial tension tests of prismatic SFRC specimens are also included in this study to support the experimental project and to verify the proposed model. Comparing the numerical results with the experimental ones it is revealed that the proposed model is efficient and accurately captures the crucial aspects of the response, such as the SFRC tension softening effect, the load versus deformation cyclic envelope and the influence of the fibers on the overall hysteretic performance. The findings of this study also reveal that SFRC beams showed enhanced cyclic behavior in terms of residual stiffness, load-bearing capacity, deformation, energy dissipation ability and cracking performance, maintaining their integrity through the imposed reversal cyclic tests.

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.

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.

This study describes two statistical methodologies to estimate the postseismic damage status of structures based on seismic parameters as novel combined procedures in earthquake engineering. Thus, a multilinear regression analysis and discriminant analysis are utilized considering twenty seismic parameters. Overall damage indices describe the postseismic damage status. Nonlinear dynamic analyses furnish the damage indices, which are considered as exact indices and references for the subsequent study. The aim is to approximate the postseismic damage indices or the damage grade of buildings using statistical methods, thus avoiding complex nonlinear dynamic analyses. The multilinear regression procedure evaluates the damage indices explicitly, and the discriminant analysis furnishes the damage grade of the structures. The proposed methods are applied to a frame structure. A set of 400 natural accelerograms is used for the training phase of the models. The quality of the models is tested initially by the same set of natural accelerograms and then by a blind prediction using a second set of synthetic accelerograms. The results of both proposed methods have shown a correct classification percentage ranging from 87.75% to 97.50% and from 70% to 90% for the sets of the natural and synthetic accelerograms, respectively.

A variety of structural members and non-structural components, including bridge piers, museum artifacts, furniture, or electrical and mechanical equipment, can uplift and rock under ground motion excitations. Given the inherently non-linear nature of rocking behavior, employing machine learning algorithms to predict rocking response presents a notable challenge. In the present study, the performance of supervised ML algorithms in predicting the maximum seismic response of free-standing rigid blocks subjected to ground motion excitations is evaluated. As such, both regression and classification algorithms were developed and tested, aiming to model the finite rocking response and rocking overturn. From this point of view, it is essential to estimate the maximum rocking rotation and to efficiently classify its magnitude by successfully assigning respective labels. To this end, a dataset containing the response data of 1100 rigid blocks subjected to 15,000 ground motion excitations, was employed. The results showed high accuracy in both the classification ($95\%$ accuracy) and regression (coefficient of determination $R^2=0.89$) tasks.