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Isolated bridges are commonly designed in the near-fault region to balance excessive displacement and seismic force. Optimal intensity measures (IMs) of probabilistic seismic demand models for isolated bridges subjected to pulse-like ground motions are identified in this study. Four typical isolated girder bridge types with varied pier height (from 4 m to 20 m) are employed to conduct the nonlinear time history analysis. Totally seven structure-independent IMs are considered and compared. Critical engineering demand parameters (EDPs), namely, pier ductility demands and bearing deformation along the longitudinal and transverse directions, are recorded during the process. In general, PGV tends to be the optimal IM for isolated bridges under pulse-like ground motions based on practicality, efficiency, proficiency, and sufficiency criterions. The results can offer effective guidance for the optimal intensity measure selection of the probabilistic seismic demand models (PSDMs) of isolated bridges under pulse-like ground motions.

The standard particle swarm optimization (PSO) algorithm converges very fast, while it is very easy to fall into the local extreme point. According to waiting effect among particles with mean-optimal position(MP), the quantum-behaved particle swarm optimization (QPSO) algorithm can prevent the particle prematurely from falling into local extreme point, but its convergence speed is slow and convergence precision is still low. In order to further improve the precision of QPSO algorithm, the evaluation method of $delta$ trap characteristic length $L(t)$ of wave function for describing the particle's state is modified. In QPSO, a random weight to each particle in swarm is introduced, and according to the order of the each particle best position's fitting value, there are three evaluation programs for $L(t)$, which are random-weight mean-optimal position(RMP), reverse-order random-weight mean-optimal position(RRMP) and same-order random-weight mean-optimal position(SRMP). Through the test of several typical functions, its result shows that the convergence accuracy of QPSO with RMP and RRMP is better than those of QPSO with MP, so the evaluation of $L(t)$ with RMP and RRMP is feasible and effective

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