Bartosz Miller

Assessment of elastic–plastic material parameters comparatively by three procedures based on indentation test and inverse analysis

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.

Synergic Combinations of Computational Methods and Experiments for Structural Diagnoses

The mechanical characterization of materials and the non-destructive assessment of possible damages in industrial plant components and in civil engineering structures and infrastructures is a problem which at present arises more and more frequently and acquires growing importance in both experimental and computational mechanics. The survey presented here concerns some representative, practically meaningful typical problems of this kind recently or currently tackled by our research team. It is intended to evidence the central role played by computer methods and by procedures of numerical mathematics, including soft computing, for the practical solutions of inverse analysis problems in real-life situations. The engineering applications dealt with herein concern steel pipelines and metal industrial components typical of the oil industry and existing large concrete dams possibly deteriorated by physico-chemical processes like alkali-silica reactions.

Application of semi-Bayesian neural networks in the identification of load causing beam yielding

Possible yielding of the cross-section of a structure might significantly decrease the safety margin of the investigated structure. The cross-section yielding causes a change of structure stiffness and, further, dynamic characteristics. The measurement of the changes of the dynamic parameters may provide information necessary to identify the load causing yielding of the cross-section, and further the yielding index (calculated when the load causing yielding is known) enables evaluation of structure safety margin. In the paper the semi-Bayesian neural networks are utilized to solve the identification problem.

Neural Networks in Updating of Dynamic Models with Experimental Verification

This paper presents the application of artificial neural networks in updating of dynamic models of engineering structures. There are presented examples of updating of two models: a model of a beam hung on two strings and a model of a portal frame. There are used multi-layer feed-forward networks and networks with radial basis function, the input vectors consist of preprocessed data obtained from the measurements done on a laboratory models of considered structures.

Identification of load parameters for an elastic-plastic beam basing on dynamic characteristics changes

Single load parameters are identified on the base of changes of known dynamic characteristics of an elastic-plastic steel beam. It is also loaded by a control load in order not to involve characteristics of the initial structure. Special attention is paid to the location of measurement points to obtain accuracy of computations corresponding to possibilities of planned measurement devices. Finite Element Method was used for the simulation of dynamic characteristics and Standard Neural Networks were applied for the inverse analysis. The main goal of the paper is the formulation of a new non-destructive method in the area of health monitoring of civil engineering structures.