This paper presents a damage identification method for offshore jacket platforms using partially measured modal results and based on artificial intelligence neural networks. Damage identification indices are first proposed combining information of six modal results and natural frequencies. Then, finite element models are established, and damages in structural members are assumed by reducing the structural elastic modulus. From the finite element analysis for a training sample, both the damage identification indices and the damages are obtained, and neural networks are trained. These trained networks are further tested and used for damage prediction of structural members. The calculation results show that the proposed method is quite accurate. As the considered measurement points of the jacket platform are near the waterline, the prediction errors keep below 8% when the damaged members are close to the waterline, but may rise to 16.5% when the damaged members are located in deeper waters.

Differences between the practical suspended-dome and the corresponding numerical model are inevitable. To reduce the existing discrepancy, model updating of a suspended-dome was investigated using the back-propagation network in the article. The article first proposed a method to increase the prediction precision of back-propagation network: reducing the range of the training data for the back-propagation network according to the previous prediction results continuously. Then, some parameters that can be measured are updated by the corresponding measured values directly, and other parameters that cannot be directly measured are updated by the corresponding prediction values from back-propagation network. The results indicate that the updated model can predict the experimental model perfectly, and back-propagation network is effective and accurate to predict the given parameters that cannot be described by an algorithm. The results also confirm that the proposed method to increase the prediction precision of back-propagation network is valid.

This paper proposes a simple approach for force finding analysis of suspended-domes based on the superposition principle. First, the feasibility of the superposition principle in a suspended-dome during tension is validated using an experimental model and the corresponding numerical model. Following that, a simplified computational method for force finding based on the superposition principle is presented. Taking a suspended-dome as an illustrative example, the initial strain at zero state is calculated by force finding, and the difference between the simplified computational method and the iterative method is investigated. The results show that the superposition principle is nearly feasible during tension and the proposed simple approach is accurate and can easily obtain the actual initial pre-stress of a suspended-dome.