Structural health monitoring (SHM) typically involves sensor deployment, data acquisition, and data interpretation, commonly implemented via a tedious wired system. The information processing in current practice majorly depends on electronic computers, albeit with universal applications, posing challenges such as high energy consumption and low throughput due to the nature of digital units. In recent years, there has been a renaissance interest in shifting computations from electronic computing units (e.g., Graphics Processing Unit) to the use of real physical systems, a concept known as physical computation. This approach provides the possibility of thinking out of the box for SHM, seamlessly integrating sensing and computing into a pure-physical entity, without relying on external electronic power supplies, thereby properly coping with resource-restricted scenarios. The latest advances of metamaterials (MM) hold great promise for this proactive idea. In this paper, we introduce a metamaterial-based sensor (termed as MM-sensor) for physically processing structural vibration information to perform designated SHM tasks, such as structural damage warning (binary classification). The decision boundary of the binary classification is programmable via a proposed inverse design framework. The MM-senosr minimizes the need for further information processing or resource-consuming operations by enabling in-situ data acquisition and analysis directly at the sensing node. We adopt the configuration of a locally resonant metamaterial plate (LRMP) to achieve the first fabrication of the MM-sensor. We take advantage of the bandgap properties of LRMP to physically differentiate the dynamic behavior of structures before and after damage. By inversely designing the geometric parameters, our current approach allows for adjustments to the bandgap features. This is particularly effective for engineering systems with a first natural frequency ranging from 9.54 Hz to 81.86 Hz; a wider range can be achieved with extended design choices. Both simulations and laboratory experiments were conducted to validate the applicability of the proposed MM-sensor, with a binary damage classification metric of over 93% through a purely physical mechanism. This success demonstrates the realization of mechanical in-sensor computing for SHM.

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