Abstract

An approach is presented for studying the mechanical behavior of soft biological tissue using the finite‐element method and a general constitutive model. The overall computational approach is used for the analysis of the mechanical behavior of the urinary bladder. Bladder tissue is modeled as a composite material formed by a soft matrix reinforced with preferentially oriented fibers. A procedure for identifying the mechanical properties of the main constituents of the bladder tissue by an inverse method is detailed. The mechanical parameters are used for the numerical simulation of the mechanical behavior of the bladder during filling within the finite‐element method.

Abstract

Mechanical factors such as stresses and strains play a major role in the growth and remodelling of soft biological tissues. The main constituents of tissue undergo different processes reacting to mechanical stimulus. Thereby, the characterisation of growth and remodelling requires an accurate estimation of the stresses and strains of their main components. Many soft tissues can be considered as composite materials and can be analysed using an appropriate rule of mixtures. Particularly, arterial tissue can be modelled as an isotropic soft matrix reinforced with preferentially oriented collagen fibres. An inverse approach to obtain the mechanical characterisation of each main component is proposed in this work. The procedure is based on a rule of mixtures raised in a finite deformation framework and generalised to include kinematics and compatibility equations for serial–parallel behaviour. This methodology allows obtaining the stress–strain relationship of the components fitting experimental data.

Abstract

An optimization method to identify the material parameters of composite structures using an inverse method is proposed. This methodology compares experimental results with their numerical reproduction using the finite element method in order to obtain an estimation of the error between the results. This error estimation is then used by an evolutionary optimizer to determine, in an iterative process, the value of the material parameters which result in the best numerical fit. The novelty of the method is in the coupling between the simple genetic algorithm and the mixing theory used to numerically reproduce the composite behavior. The methodology proposed has been validated through a simple example which illustrates the exploitability of the method in relation to the modeling of damaged composite structures.

Abstract

This paper proposes a technique for the preliminary identification of pipes obstructed by blockages within a pipeline or a complex pipe network, relying only upon few measurements that are usually gathered to monitor the infrastructure operability. Blockages are identified as a result of an optimization procedure that minimizes the discrepancy between measured and simulated flow quantities. The framework exploits a Finite Element-like simulator of the flow problem in the system of pipes and Genetic Algorithms to perform the optimization procedure. Thermal properties of the fluid flowing inside the pipe system are considered. Results of the identification procedure are presented, and advantages and disadvantages of the procedure for the on-field implementation are outlined.