Mayank Mishra

TL;DR: In this paper , the authors summarized the applications of the wireless IoT technology in the monitoring of civil engineering infrastructure and discussed several case studies on real structures and laboratory investigations for monitoring the structural health of real-world constructions.

TL;DR: In this paper, a systematic review of the various machine learning techniques applied to assess the health condition of heritage buildings is presented, which can be used for several predictive applications such as predicting the compressive strength of masonry or repair mortars, possible damage scenarios in heritage buildings, seismic vulnerability assessment, determination of the mechanical properties of materials, and superficial damages on the surface of the monument due to weathering effects, material loss, efflorescence, seepage, algae growth, and moss deposition.

TL;DR: The recently proposed ant lion optimiser, which is a population-based search algorithm, mimicked the hunting behaviour of antlions, was used for assessing structural damage and indicated that the proposed algorithm required fewer parameters than other metaheuristic algorithms to identify the location and extent of damage.

TL;DR: In this article, a methodology based on the Bayesian data fusion techniques applied to non-destructive and destructive tests for the structural assessment of historical constructions is presented, which considers several levels of uncertainty since the parameters of interest are considered random variables with random moments.

TL;DR: The analyses revealed that machine learning techniques are robust, can successfully be used for the prediction of the remaining compressive strength of historical constructions, and thus can provide decision support for inspection professionals.