In this paper an experimental – numerical methodology is proposed for the in situ assessment of possibly deteriorated elastic properties of dam concrete and for the estimation of the local stress state in a concrete dam. The methodology described herein consists of the following operative stages: excavation of two parallel small holes (instead of the single one generally employed for rock testing); measurements of diameter variations in both holes by dilatometers; combination of experimental tests with computer simulations and inverse analyses for the parameter identification by means of artificial neural networks. Numerical validation tests of this parameter identification methodology are presented and its novelties and potentialities are discussed.

Non-destructive indentation tests are more and more frequently employed for the mechanical characterization of structural metals. Three kinds of experimental data sets as inputs to inverse analyses for parameter identification are comparatively examined in this article: (A) indentation curve, namely the relationship between penetration of the indenter tip versus the force applied on it; (B) both this curve and imprint geometry; (C) imprint profile only. The comparisons are based on two different parameter identification procedures. The novel information source (C) turns out to be promising and advantageous in practical industrial applications for innovative diagnostic analysis methods centred on indentation.

This paper presents a novel method for the maximization of eigenfrequency gaps around external excitation frequencies by stacking sequence optimization in laminated structures. The proposed procedure enables the creation of an array of suggested lamination angles to avoid resonance for each excitation frequency within the considered range. The proposed optimization algorithm, which involves genetic algorithms, artificial neural networks, and iterative retraining of the networks using data obtained from tentative optimization loops, is accurate, robust, and significantly faster than typical genetic algorithm optimization in which the objective function values are calculated using the finite element method. The combined genetic algorithm–neural network procedure was successfully applied to problems related to the avoidance of vibration resonance, which is a major concern for every structure subjected to periodic external excitations. The presented examples illustrate a combined approach to avoiding resonance through the maximization of a frequency gap around external excitation frequencies complemented by the maximization of the fundamental natural frequency. The necessary changes in natural frequencies are caused only by appropriate changes in the lamination angles. The investigated structures are thin-walled, laminated one- or three-segment shells with different boundary conditions.

Composite shells find diverse applications across industries due to their high strength-to-weight ratio and tailored properties. Optimizing parameters such as matrix-reinforcement ratio and orientation of the reinforcement is crucial for achieving the desired performance metrics. Stochastic optimization, specifically genetic algorithms, offer solutions, yet their computational intensity hinders widespread use. Surrogate models, employing neural networks, emerge as efficient alternatives by approximating objective functions and bypassing costly computations. This study investigates surrogate models in multi-objective optimization of composite shells. It incorporates deep neural networks to approximate relationships between input parameters and key metrics, enabling exploration of design possibilities. Incorporating mode shape identification enhances accuracy, especially in multi-criteria optimization. Employing network ensembles strengthens reliability by mitigating model weaknesses. Efficiency analysis assesses required computations, managing the trade-off between cost and accuracy. Considering complex input parameters and comparing against the Monte Carlo approach further demonstrates the methodology’s efficacy. This work showcases the successful integration of network ensembles employed as surrogate models and mode shape identification, enhancing multi-objective optimization in engineering applications. The approach’s efficiency in handling intricate designs and enhancing accuracy has broad implications for optimization methodologies.