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Structural condition assessment is crucial for ensuring the safe services of structures, with structural damage detection (SDD) being a core component. In this paper, a novel SDD method is proposed based on the adaptive grasshopper algorithm and sparse regularization. It aims to tackle accuracy decline of SDD results and instability involving uncertainties and incomplete measurement, thereby achieving sparse-regularization-based structural condition assessment. Firstly, adaptive Lévy flight and elite opposition-based learning strategies are incorporated into the adaptive grasshopper algorithm to prevent the SDD process from falling into local optima and to enhance the stability of SDD results. Secondly, a modal parameter-based objective function with sparse regularization is formulated to increase the sparsity of SDD results, thereby improving SDD accuracy and robustness. The optimization results of competition-based evolutionary computation benchmark functions show that the adaptive grasshopper algorithm exhibits better global convergence and identification stability compared with its standard version. Numerical and experimental results for simply-supported beams indicate that the proposed method can ensure reliable SDD accuracy even in the case of incomplete measurements, and it possesses good noise robustness as well.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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