Author

Liupengfei Wu

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Open access Aug 2026

Autonomous post-typhoon structural inspection and damage assessment: an agentic AI and aerial robotics-enabled model

The increasing frequency and intensity of typhoons in recent years due to climate change has heightened the need for rapid post-disaster assessment. Inspection of post-typhoon damage is required to assess damages to critical infrastructure and facilitate disaster management. Unmanned aerial vehicles (UAVs) are commonly used for the recognition of post-typhoon damage. However, existing UAVs require either to be manually teleoperated or to follow a pre-programmed flight path, which is significantly limited in terms of rapid or adaptive post-typhoon inspection. To address this critical limitation, this study introduces a novel agentic AI and aerial robotics-enabled model designed for autonomous inspection operations. We propose a three-layered conceptual framework that closes the perception–cognition–action loop, thereby enabling goal-directed mission reasoning. This framework is operationalized as the Agentic AI and UAV-enabled Autonomous Inspection and Assessment System (AAUIAS)—a comprehensive cyber-physical system architecture. Within this architecture, the core agentic AI layer independently performs world modeling, real-time damage assessment, and dynamic path replanning. The proposed model and system were evaluated through high-fidelity simulations of a typhoon-impacted urban environment. Results demonstrate that AAUIAS reduces overall inspection time by 34.5%–40.2% relative to conventional approaches, achieves robust dynamic obstacle avoidance, and intelligently prioritizes critical damage sites. This preliminary work contributes a conceptual model and system architecture, supported by simulation-based evidence, for transforming UAVs into proactive intelligent agents, thereby advancing the state of the art in autonomous disaster response and resilient infrastructure management.

Liupengfei Wu, L. Geng, Jin Xue et al. · AI in Civil Engineering · 0 citations

Preprint Aug 2026

FuncRoom-Agent: Sequential Feed-Forward 3D Functional Indoor Scene Generation

We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than merely visually plausible layouts. Existing agentic and executable methods improve controllability, but often depend on costly test-time generate--evaluate--revise loops, making functional room generation slow and computationally expensive. We address this challenge with three technical contributions. First, we design a recursive domain-specific language to effectively organize the hierarchical object compositions required by functional rooms, from room structure and major furniture to dense support-surface and nested small objects. It represents rooms as staged executable programs with explicit geometric and functional relations. Second, we propose a sequential feed-forward scene construction framework that distills recursive construction traces into a scene construction expert. At inference time, the expert writes executable DSL code stage by stage, and a deterministic executor directly instantiates each stage without teacher agents, online critics, or iterative repair. Third, we introduce ScenePRM, an execution-grounded process reward framework that improves the expert through reinforcement learning with functional, geometric, relational, and future-constructability feedback. We further establish a function-oriented benchmark and show state-of-the-art performance on both general indoor scene generation and function-room generation, achieving stronger functional completeness, relation correctness, geometric executability, and generation efficiency.

Hao Feng, Zhi Zuo, Ming-Jian Liang et al. · 1 citation