Harnessing the power of natural evolution for automated exploration of novel forms of metastructures is likely to be the next technological revolution of the material science. Herein, the principles of evolution into the metamaterial design and discovery process to directly evolve thousands of metastructures with hitherto-unknown structures and new modalities of operation are embedded. In this so-called evolving metamaterial (EM) concept, evolution takes place by randomly creating an initial population of parent metamaterial entities that pass on their genetic material to their offspring through variation, reproduction, and selection. The metamaterial configurations with desired response emerge during this evolutionary process. The EM concept presents a different approach for direct morphological evolution of metamaterial microstructures using merely a piece of matter. For the biologically inspired evolution of mechanical metamaterials, this piece is chosen to be a representative unit cell to launch the design process. This paradigm shift by creating an evolutionary computational framework for the exploration of a series of proof-of-concept 2D mechanical metamaterial structures with maximum bulk modulus, maximum shear modulus, and minimum Poisson's ratio is studied. The capability of the proposed approach for discovering 3D is examined by exploring a suite of 3D configurations with maximum bulk modulus.

Delamination is one of the most critical defects assessed during bridge deck inspections. Recently, infrared (IR) thermography has gained more attention for delamination detection since it provides fast and effective inspections with reasonable accuracy. However, point-by-point inspections with handheld IR cameras and manual data interpretation are still time consuming. In addition, manual data interpretation is highly dependent on the inspectors’ experiences. To tackle these concerns, this study conducted investigations from two perspectives to improve IR-based delamination detection: (1) data collection and (2) data interpretation. In this study, unmanned aerial vehicles (UAVs) equipped with IR sensors have been deployed to perform automated inspection data collection. Various factors have been considered to develop a preliminary UAV-based IR data collection plan. The developed data collection plan has been implemented on a full-scale bridge deck specimen at the Bridge Evaluation and Accelerated Structural Testing (BEAST) facility. Discussions and suggestions based on the results have been provided. A pixel-level deep learning method is developed for automatic delamination detection and quantification of the bridge deck. IR data collected from four real bridge decks are pixel-wise labeled and used for model calibration. The accuracy and mean intersection over union achieve 99.36%, 97.96%, 97.83% and 0.98, 0.96, 0.95 for training, validation, and testing datasets, respectively. Furthermore, an easy-to-use tool is developed based on the proposed method for practical implementation. The developed tool is validated using the BEAST specimen data. The fast and accurate implementation of the developed tool makes it a promising option for autonomous bridge deck inspection.

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