Cantilevered piezoelectric energy harvesters have been extensively investigated in the literature of energy harvesting. As an alternative to conventional cantilevered beams, this article presents the L-shaped beam-mass structure as a new piezoelectric energy harvester configuration. This structure can be tuned to have the first two natural frequencies relatively close to each other, resulting in the possibility of a broader band energy harvesting system. This article describes the important features of the L-shaped piezoelectric energy harvester configuration and develops a linear distributed parameter model for predicting the electromechanically coupled voltage response and displacement response of the harvester structure. After deriving the coupled distributed parameter model, a case study is presented to investigate the electrical power generation performance of the L-shaped energy harvester. A direct application of the L-shaped piezoelectric energy harvester configuration is proposed for use as landing gears in unmanned air vehicle applications and a case study is presented where the results of the L-shaped — energy harvester — landing gear are favorably compared against the published experimental results of a curved beam configuration used for the same purpose.

Modeling and simulation have been extensively used to solve a wide range of problems in structural engineering. However, many simulations require significant computational resources, resulting in exponentially increasing computational time as the spatial and temporal scales of the models increase. This is particularly relevant as the demand for higher fidelity models and simulations increases. Recently, the rapid developments in artificial intelligence technologies, coupled with the wide availability of computational resources and data, have driven the extensive adoption of machine learning techniques to improve the computational accuracy and precision of simulations, which enhances their practicality and potential. In this paper, we present a comprehensive survey of the methodologies and techniques used in this context to solve computationally demanding problems, such as structural system identification, structural design, and prediction applications. Specialized deep neural network algorithms, such as the enhanced probabilistic neural network, have been the subject of numerous articles. However, other machine learning algorithms, including neural dynamic classification and dynamic ensemble learning, have shown significant potential for major advancements in specific applications of structural engineering. Our objective in this paper is to provide a state-of-the-art review of machine learning-based modeling in structural engineering, along with its applications in the following areas: (i) computational mechanics, (ii) structural health monitoring, (iii) structural design and manufacturing, (iv) stress analysis, (v) failure analysis, (vi) material modeling and design, and (vii) optimization problems. We aim to offer a comprehensive overview and provide perspectives on these powerful techniques, which have the potential to become alternatives to conventional modeling methods.