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Earthquakes are one of the most frequent and inevitable natural disasters that occur on Earth. The most important seismic isolation device used in the isolation technology is the isolation bearing, but the limited structure of the isolation bearing is not suitable for the seismic isolation and shock absorption of smart transportation buildings. Therefore, it is very important to improve the new generation of seismic isolation structures with good seismic isolation effect, stable performance, and economical performance. It is very important to study the structural composition of the isolation bearing and the rigidity of the isolation structure. Isolation bearings are designed to resist and absorb the energy of seismic shocks by installing substructures in the structure. Active control involves installing sensors on the structure and its foundation to determine how the structure responds to seismic action. In this paper, based on the intelligent transportation of the sensor network, the reticulated shell structure of the isolation bearing is analyzed. By introducing the architecture and network layout of the sensor network, it is beneficial to obtain more accurate seismic data in complex and difficult terrain. This paper analyzes the technical principle of seismic isolation technology, which can effectively avoid the upward transmission of ground vibration by increasing the flexibility and proper damping of the system. From the experimental data of the seismic response of the ground-isolated structure to the near-field pulsation and far-field vibration, the total energy of the ground-isolated structure under the near-field pulsed ground motion is the largest. The seismic isolation effect of the reticulated shell structure of the seismic isolation bearing prevents more than 80% of the seismic energy from being transmitted to the superstructure. [ABSTRACT FROM AUTHOR]

• Using an Artificial Neural Network- Developed Collective Animal Behavior Algorithm for reducing the problem complexity. • The failure cases of the structures have found and used for train databases. • The correlation among structural response as input and structural stiffness as output is provided utilizing ANN-DCABA. • The provided ANN-DCABA is so efficient and produces advisable precision in structural failure discovery under a set of ground motions. • The Artificial Neural Network- Developed Collective Animal Behavior Algorithm defines the failure of the intended structure. The seismic design contains considerable uncertainties, which originate from the structural geometries, earthquake motions, analytical models, and material properties. By considering all main uncertainties, reliability analysis is applied estimating the possibility of failure in each of a set of performance requirements. Structural failure localization, quantification, and failure possibility evaluation are principal outputs in the structures reliability evaluation. In this paper, an Artificial Neural Network- Developed Collective Animal Behavior Algorithm is used for reducing the problem complexity needed for reliability investigation and failure detect. In this way, a definite structure is modeled and some failure case are determined. These cases are recognized as train databases for organizing the ANN-DCABA. Therefore, the correlation among structure's response as input and structure's stiffness as output is provided utilizing Artificial Neural Network- Developed Collective Animal Behavior Algorithm. The provided method is so efficient and produces advisable precision in structural failure discovery under a series of ground motions. Besides, for assessing the structure reliability, 5 accidental variables are determined. These are columns' state of the 1st, 2nd, and 3rd floor, gravity loads, and elasticity modulus. Finally, the Artificial Neural Network- Developed Collective Animal Behavior Algorithm specifies the failure probability of the proposed structure and it can reduce the computational effort required for reliability analysis and damage detection. However, MCS could indicate the failure probability for a given structure; the Artificial Neural Network- Developed Collective Animal Behavior Algorithm helps simulation techniques for receiving an acceptable precision and decrease computational effort. [ABSTRACT FROM AUTHOR]