Michał Gawlicki, M.Sc., Eng. 

Recent publications
1.  Gawlicki M., Stany krytyczne w ciałach z tarciem wewnętrznym, Prace IPPT  IFTR Reports, ISSN: 22993657, No.25, pp.182, 1986 
Conference papers
1.  Gawlicki M., Jankowski Ł., Multiobjective optimization for identification of a moving load path, SMART 2019, 9th ECCOMAS Thematic Conference on Smart Structures and Materials, 20190708/0711, Paris (FR), pp.215222, 2019 Abstract: This contribution presents an approach for indirect identification of the 2D path of a moving load. A multicriterial formulation is proposed, where one objective function quantifies the mismatch between the measured and the simulated structural response. The second objective function expresses the natural expectation that the paths of moving loads are continuous and relatively smooth, and it expresses thus a certain splinebased measure of the geometric regularity of the path. The Pareto front is determined in a local evolutionary search and used to strike the balance between the response fit and the geometric regularity of the path. The approach is tested in a laboratory experimental setup of a plate loaded by a linefollower robot. It is found that the implementation of the smoothnessbased objective has a regularizing influence on the identification results: it reveals and emphasizes the actual geometrical character of the identified paths. Keywords:Trajectory Identification, Moving Load, Inverse Problem, Structural Health Monitoring, Multicriterial Optimization Affiliations:
 
2.  Gawlicki M., Jankowski Ł., Identification of moving loads using the l1 norm minimization, CMM 2017, 22nd International Conference on Computer Methods in Mechanics, 20170913/0916, Lublin (PL), DOI: 10.1063/1.5019092, Vol.1922, pp.10000719, 2018 Abstract: This contribution deals with the inverse problem of indirect identification of moving loads. The identification is performed based on the recorded response of the loaded structure and its numerical model. A specific feature of such problems is a very large number of the degrees of freedom (DOFs) that can be excited and a limited number of available sensors. As a result, unless the solution space is significantly limited, the identification problem is underdetermined: it has an infinite number of exact, observationally indistinguishable solutions. We propose an approach based on the assumption of sparsity of the excitation, which can be expressed in the form of a requirement of a bounded l1 norm of the solution. As long as the loads are sparse, the approach allows them to be freely moving, without the usual assumption of a constant velocity. We first test the approach in a numerical example with 10% rms measurement noise. A good qualitative agreement of the numerical results allows to proceed with experimental investigations, and the moving load identification is then carried out based on the response measured experimentally on a lab test stand. Affiliations:

Conference abstracts
1.  Gawlicki M., Jankowski Ł., Identification of a moving load 2D path under insufficient instrumentation, IPM 2019, 5th International Conference on Inverse Problems Methods, 20190522/0524, Kombornia (PL), pp.12, 2019 Abstract: This contribution is devoted to the problem of indirect identification of 2D trajectories of moving loads based on the measured mechanical responses of the loaded structure. This is an inverse problem of load identification, and such problems have been intensively studied. Such problems are typically characterized by (1) a very large number of structural degrees of freedom that can be excited by the moving load and (2) a limited number of sensors that are used to measure the response. In effect, the na¨ıve formulation based on minimization of the residuum norm is underdetermined, and the corresponding identification problem has an infinite number of exact solutions. Thus, in order to guarantee the uniqueness of the solution, the generality of the load is typically limited by assuming that the trajectory of the moving load is known (most often, the problem is reduced to the case of a single vehicle moving over a 1D bridge at a constant velocity) and that only the magnitude of the load is subject to identification. In contrast, our aim here is to identify more general loads, and in particular trajectories of loads that are freely moving on 2D structures like plates. Keywords:moving load identification, inverse problem, trajectory identification Affiliations:
 
2.  Gawlicki M., Jankowski Ł., Identification of a load moving on a plate using the l1 norm minimization, SolMech 2018, 41st SOLID MECHANICS CONFERENCE, 20180827/0831, Warszawa (PL), No.P226, pp.12, 2018 Abstract: There are two fundamental inverse problems in the field of structural health monitoring (SHM): identification of damages and identification of loads. Effectiveness of the related computational methods is crucial for maintaining integrity of the monitored structures. This contribution considers identification of unknown loads based on measurements of structural response. It is a relatively extensively researched problem: reviews of techniques used for offline load identification can be found in [1,2], while techniques for online identification are reviewed in [3].
 
3.  Gawlicki M., Jankowski Ł., Identification of moving loads using the l1 norm minimization, CMM 2017, 22nd International Conference on Computer Methods in Mechanics, 20170913/0916, Lublin (PL), pp.12, 2017 Abstract: This contribution deals with the inverse problem of indirect identification of moving loads. The identification is performed based on the recorded response of the loaded structure and its numerical model. A specific feature of such problems is a very large number of the degrees of freedom (DOFs) that can be excited and a limited number of available sensors. As a result, unless the solution space is significantly limited, the identification problem is underdetermined: it has an infinite number of exact, observationally indistinguishable solutions. We propose an approach based on the assumption of sparsity of the excitation, which can be expressed in the form of a requirement of a bounded l1 norm of the solution. As long as the loads are sparse, the approach allows them to be freely moving, without the usual assumption of a constant velocity. We test the approach in a numerical example with 10% rms measurement noise and describe an experimental setup that is being prepared to perform experimental verification. Keywords:inverse problems, structural mechanics, moving load identification, sparsity, l1 norm Affiliations:
 
4.  Gawlicki M., Jankowski Ł., Identification of moving loads via l1constrained solutions, ECCOMAS  IPM 2017, 4th International Conference on Inverse Problems in Mechanics of Structures and Materials, 20170531/0602, Rzeszów  Krasiczyn (PL), pp.2526, 2017 Abstract: Indirect identification of moving loads based on the measured response is one of the crucial problems in structural health monitoring. It is important in automated assessment of structures and pavements, in traffic monitoring and control, and as a prerequisite for structural control. As such, it has been intensively researched. An important difficulty is that a moving load can excite a very large number of structural Dofs, which all have to be taken into account in the identification procedure based on measurements of a much more limited number of sensors. A straightforward formulation yields thus an underdetermined problem with an infinite number of solutions. Therefore, in most of the approaches so far, the solution space is significantly limited by the assumption that the load corresponds to a single vehicle moving at a constant velocity, which excludes loads of a more general nature (e.g., multiple loads). However, instead of limiting the solution space, it can be noted that in practice moving loads are sparse in time and space, which fits the framework of compressed sensing. Such an a priori knowledge of sparsity is typically expressed by limiting the l1 norm of the solution. To our knowledge, although used in other contexts, the concept has not been applied so far for identification of moving loads. The approach is tested in a numerical example with 10% rms measurement noise. Experimental work is in progress. Affiliations:
