This article presents results of an investigation that focuses on assessing bondline strength of carbon/epoxy fiber reinforced polymeric (CFRP) precured strips embedded in concrete for near-surface-mounting (NSM) strengthening applications. In this study, experimental evaluation and nonlinear numerical analysis of CFRP/concrete interfacial bond behavior are investigated. In this study, 15 pullout tests were performed on different reinforced concrete (RC) U-shaped column specimens with different NSM groove configurations to evaluate the effect of groove size, groove depth, and adhesive bondline thickness on overall performance of the CFRP-NSM system and to identify the optimum groove dimensions. Experimental results indicated that changing groove geometries of the NSM-FRP reinforcements significantly affect bondline strength and associated mode of failures of NSM-CFRP system. It was also shown that increasing bondline length lead to an increase in ultimate failure load and CFRP rupture strain. Based on results of this study, NSM materials with lower longitudinal moduli and higher rupture strains (e.g., E-glass/epoxy or basalt/epoxy), may increase toughness and enhance performance of the NSM strengthening system.

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This paper presents results of a study that focuses on developing a genetic algorithm (GA) for multi-criteria optimization of orthotropic, energy-efficient cementitious composite sandwich panels (CSP). The current design concept of all commercially produced CSP systems is based on the assumption that such panels are treated as doubly reinforced sections without the consideration of the three-dimensional truss contribution of the orthotropic panel system. This leads to uneconomical design and underestimating both the strength and stiffness of such system. In this study, two of the most common types of commercially produced sandwich were evaluated both numerically and experimentally and results were used as basis for developing a genetic algorithm optimization process using numerical modeling simulations. In order to develop a sandwich panel with high structural performance, design optimization techniques are needed to achieve higher composite action, while maintaining the favorable features of such panels such as lightweight and high thermal insulation. The study involves both linear and nonlinear finite element analyses and parametric optimization. The verification and calibration of the numerical models is based on full-scale experimental results that were performed on two types of commercially produced sandwich panels under different loading scenarios. The genetic algorithm technique is used for optimization to identify an optimum design of the cementitious composite sandwich panels. The GA technique combines Darwin’s principle of survival of fittest and a structured information exchange using randomized crossover operators to evolve an optimum design for the cementitious sandwich panel. Parameters evaluated in the study include: (i) shear connectors’ geometry, its volume fraction and distribution; (ii) exterior cementitious face sheets thickness and (iii) size and geometry steel wires reinforcements. The proposed optimization method succeeded in reducing cost of materials of CSP by about 48% using genetic algorithm methodology. In addition, an optimized design for CSP is proposed that resulted in increasing the panel’s thermal resistance by 40% as compared to existing panels, while meeting ACI Code structural design criteria. Pareto-optimal front and Pareto-optimal solutions have been identified. Correlation between the design variables is also verified and design recommendation are proposed.