This paper presents a maintenance strategy optimization method for RC girder bridges by considering the performance indicators, service life and life cycle maintenance cost, as criteria. The condition and reliability indices are defined as performance indicators. The deteriorated processes of performance indicators with and without maintenance actions are described as multilinear models. The life cycle maintenance planning optimization of deteriorating bridges is formulated as a multiobjective problem to be solved by an improved nondominated sorting genetic algorithm with controlled elitism. The condition index, reliability index, service life, and life cycle maintenance cost of the bridge are considered as four separate objective functions. A simply supported RC girder bridge is analyzed as an application example to demonstrate the usefulness of the proposed procedure.

In the context of complex operational environments and limited sensor configurations, modal identification of large-scale tower structures often faces challenges related to adaptive model order determination and modal aliasing. This study develops an algorithmic framework for automatic mode identification based on the corrected Akaike information criterion (AICC) and adaptive density-based clustering. First, unlike traditional singular entropy increment (SEI) methods where the determined model order is affected by cumulative thresholds, the AICC-based approach ensures that the adaptively determined model order remains stable. Furthermore, automatic model order selection using the AICC is integrated with adaptive density-based clustering, where the modal assurance criterion extended to a complex mode space (MACXP) is employed to define a modal distance metric. The proposed framework enhances automatic modal clustering and mode-shape discrimination under limited sensor conditions. Finally, a field application was carried out on A-shaped steel towers of integrated bridge–station structures in a high-speed railway station to identify and validate their dynamic characteristics. The results demonstrate that (i) AICC-based model order selection effectively overcomes the threshold dependence of SEI, ensuring improved stability and reliability; (ii) combining AICC-based order determination with density-based clustering enables stable and automated modal identification; and (iii) compared with the conventional MAC, MACXP exhibits superior mode shape discrimination capability under sparse measurement conditions and clearly reveals differences in the modal characteristics of complex structures. This study provides an effective approach for model order determination, mode discrimination, and automated modal identification of large-scale engineering structures under limited sensor deployments.