Optimisation of Modelling of Finite Element Differential Equations with Modern Art Design Theory

The bridge is one of the most symbolic and expressive public buildings in the urban landscape. Because of its open and shared use attributes, it has attracted more attention from society. In recent years, with the continuous improvement of the quality of urban construction in China, higher requirements have been put forward on the landscape and function of bridges. In the finite element model of bridge structure damage identification, health diagnosis and bridge working condition assessment and prediction, a reliable and more accurate finite element analysis model is the basis. However, most finite element models are established based on structural design drawings, implying more idealised assumptions and simplifications [1]. therefore, thalamic characteristics an

This phenomenon is a specific difference between the established finite element model and the actual structure. When the difference is significant, the calculated result of the model will be different from the actual measurement result, even exceeding the accuracy allowed in the project. In this case, the finite element model needs to be revised. Regarding the finite element model revision, scholars from various countries have conducted extensive research [2]. The general approach is to minimise the residuals of various tests/calculations of the structure. They proposed a series of structural model modification algorithms for different modification objects according to different optimisation objectives and different optimisation constraints.

This paper proposes a parametric model correction algorithm based on optimisation design theory. This algorithm takes the minimum weighted sum of the frequency residual and the measured degree of freedom residuals as the optimisation of calculation objective. The correction objects are the geometrical, physical and mechanical parameters of the components in the finite element model [3]. The algorithm imposes constraints on the modification parameters and frequency changes based on engineering experience, and further reduces the problem of finite element model modification to constrained optimisation. The problem is solved using an optimised iterative algorithm based on gradient descent.

The three-span continuous beam is shown in Figure 2. Its total length is 11 m. The span layout is 3 + 5 + 3 m. The rectangular cross-section is 0.2 m × 0.2 m. The corresponding moment of inertia is I = 1.33 × 10⁻⁴ m. The area is A = 0.04 m². The material is E = 3.0 × 10⁴ MPa. ρ = 2500 kg/m³.

Fig. 2

The experiment uses plane beam elements to establish a finite element analysis model. It is divided into 22 units and 23 nodes at equal intervals [9]. We calculated the first eight modal frequencies and modal shapes using initial structural parameters that are mentioned above. At the same time, these values are used as the calculated values of the initial structural modal parameters. To simulate the unknown parameters, we reduce the moments of inertia of units 2, 9, 12 and 20 by 30%, 40%, 50% and 40%, respectively. And the experiment increases the density of all unit materials by 20% when other parameters remain unchanged. On this basis, the first eight modal frequencies and modal shapes after the parameter change are calculated and used as the simulation measured values [10]. The starting point of the parameter correction iteration is the initial structure parameter, and the ideal target of the correction is the changed parameter value. Among them, the parameter correction iteration start point is the initial structure parameter.

Frequency error: ω%=|(ωe−ωa)ωe|% \omega \% = |{{(\omega e - \omega a)} \over {\omega e}}|\% . Vibration shape error: ‖ϕ‖12%=(‖ϕe−ϕa‖12‖ϕe‖12)% {\left\| \phi \right\|_{{1 \over 2}}}\% = \left( {{{{{\left\| {{\phi _e} - {\phi _a}} \right\|}_{{1 \over 2}}}} \over {{{\left\| {{\phi _e}} \right\|}_{{1 \over 2}}}}}} \right)\% .

The calculated and measured values of the modal parameters before model modification and their errors are shown in Table 1. It can be seen from Table 1 that the calculated value of the modal parameters has a significant error with the actual measured value [11]. For example, the maximum error of frequency is 18.26%, and the maximum error of mode shape is 38.55%. Therefore, the experiment is divided into two cases.

Case 1. The target mode adopts the first eight orders. The trimming parameters only include the moments of inertia of elements 2, 9, 12 and 20 and the material parameter density ρ. We set a total of 5 parameters.

Case 2. The target mode adopts the first eight orders. The inertia of all elements is selected as the design parameter, plus the material density ρ, a total of 23 parameters.

Modified calculation constraints: (1) Moment of inertia constraint: 5 × 10⁻⁵ ≤ Ii ≤ 1. 8 × 10⁻⁴. (2) Density constraint: 2200 ≤ ρ ≤ 3200. (3) Frequency constraint: {|ω_ei − ω_ai| ≤ 20, i = 1,2,3,4|ω_ei − ω_ai| ≤ 50, i = 5,6,7,8. The correction results are shown in Tables 2 and 3.

Tables 2 and 3 show that the calculated values of the modal parameters (frequency, mode shape) after the two cases of the proposed algorithm are very close to the measured values [12]. The maximum error of case 1 frequency is 0.05% and the maximum error of mode shape is 0.65%. The correction values of the five parameters are also very close to the actual values of the parameters. The maximum error is 1.015%. The maximum error of the case 2 frequency is 1.929%. Further, the maximum error of mode shape is 0.93%. The correction values of the five parameters are also relatively close to the actual values of the parameters. The maximum error is 2.4%. In case 1, the correction parameters are directly carried out on the five parameters with errors, and it only takes a few iterations to converge to the actual value. In case 2, there are 23 correction parameters and the algorithm can still effectively complete the model correction function [13]. However, the error of the parameter correction results has increased, and the iterative calculation workload has also increased. As shown in Figure 3, case 1 (Figure 3b) converges at the 9th iteration step, while Case 2 (Figure 3b) takes more than 15 iterations to converge.

Fig. 3

The calculation examples prove that the parametric model correction algorithm proposed in this paper is feasible and effective. Satisfactory correction results can be obtained even when there are many correction parameters. However, when the parameters increase, the efficiency of iterative calculation decreases, and the amount of calculation is reduced. Larger.

The construction of the optimised objective function considers the frequency and mode shape errors and directly uses the measured mode shape components. Thus, there is no need for a complete mode vector, and the error introduced by mode expansion is avoided. Based on the experience of modern bridge art design engineering to impose constraints on the modification parameters and frequency changes, we attribute the model modification problem to the constraint optimisation problem. The problem is solved by an iterative optimisation algorithm based on gradient descent. The result of calculations of the example proves that the model correction method proposed in this paper is feasible and effective.