Advancing Bridge Construction Monitoring: AI-Based Building Information Modeling for Intelligent Structural Damage Recognition

Figures & data

Figure 1. Structure diagram of artificial neuron.

Figure 2. General steps for forming sample data.

Figure 3. System workflow.

Figure 4. Distribution of bridge structure damage dataset.

Table 1. First 10th order self-oscillation frequency of the structure under partial damage conditions (Hz).

Order	Damage10%	Damage20%	Damage30%	Damage40%	Damage50%
1	0.676731	0.676613	0.676510	0.676373	0.676191
2	1.125434	1.125052	1.124719	1.124277	1.123700
3	1.992544	1.991280	1.990160	1.988639	1.986598
4	2.569577	2.568049	2.566703	2.564887	2.562472
5	3.458108	3.456375	3.454848	3.452789	3.450050
6	4.319442	4.319267	4.319110	4.318895	4.318604
7	4.457569	4.455445	4.453540	4.450917	4.447329
8	5.376948	5.370691	5.365119	5.357507	5.347218
9	5.700947	5.698506	5.696413	5.693668	5.690153
10	5.018453	5.984823	5.954613	5.913835	5.860353

Figure 5. Loss variation of neural network training process.

Figure 6. Prediction performance of neural network before and after improvement.

Figure 7. Comparison of the identification results of traditional NN for 50% impairment.

Figure 8. Comparison of identification results of improved NN for 50% impairment.

Table 2. Comparison of identification results of neural networks for 10% damage.

Method	Location	Actual Output					Average error
Traditional NN	A	0.107	0.005	0.013	0.001	0.016	0.77%
	B	0.009	0.092	0.011	0.017	0.004
	C	0.00	0.005	0.091	0.019	0.006
	D	0.009	0.015	0.005	0.106	0.012
	E	0.015	0.001	0.004	0.012	0.108
Improved NN	A	0.091	0.005	0.012	0.012	0.001	0.56%
	B	0.001	0.107	0.013	0.011	0.006
	C	0.000	0.005	0.101	0.007	0.012
	D	0.014	0.001	0.014	0.105	0.011
	E	0.013	0.002	0.014	0.007	0.104