Recent studies show climate-induced sea-level rise (SLR) will accelerate storm surge impacts in many coastal areas around the world. The decision-making of building-level adaptation strategies is a challenging task due to uncertain climate impacts. This study evaluates building-level adaptation strategies through a dynamic programming-based cost-benefit analysis approach to incorporate the latest information of SLR in adaptation decision-making. The adaptation outcomes are estimated by applying a Monte-Carlo method with stochastic flood damage of buildings under four SLR projections. Based on a case study in Bay County, Florida (USA), results indicate that single-family and multi-family buildings are the most vulnerable buildings in Bay County. Mobile homes have a lower flood risk, while they are more sensitive to SLR. The long-term flood damage shows SLR could exponentially increase the average annual flood damage in the community from $\mathrm{\$}17.7$ million to $\mathrm{\$}204$ million. Investing in adaptive measures can substantially mitigate building-level flood risk, where the adapted average annual damage ranges from $\mathrm{\$}9.57$ million to $\mathrm{\$}38.2$ million in the county. The proposed adaptation method could facilitate more effective risk communications between the public and private sectors and improvise community adaptation planning under uncertain SLR.

最近的研究表明，气候引起的海平面上升（SLR）将加速全球许多沿海地区的风暴潮冲击。由于不确定的气候影响，建筑层面适应策略的决策是一项艰巨的任务。这项研究通过基于动态规划的成本效益分析方法，将SLR的最新信息纳入适应决策过程，来评估建筑层面的适应策略。通过应用蒙特卡洛方法对四个SLR投影下建筑物的随机洪水破坏进行评估，得出适应结果。根据美国佛罗里达州贝县的一个案例研究，结果表明，单户和多户建筑是贝县最脆弱的建筑。移动房屋的洪水风险较低，而它们对SLR更为敏感。$\mathrm{\$}17.7$ 百万至 $\mathrm{\$}204$百万。投资适应性措施可以大大降低建筑物级别的洪水风险，适应后的年平均损失范围为$\mathrm{\$}9.57$ 百万至 $\mathrm{\$}38.2$万县。拟议的适应方法可以促进公共部门和私营部门之间更有效的风险沟通，并在不确定的SLR情况下临时制定社区适应计划。