In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures.

This paper presents a tunable planar auxetic metamaterial (PAM) for controlling and filtering acoustic waves and provides guidelines for bandgap design of the proposed PAMs. Numerical results for deformed and undeformed PAMs were obtained from several finite element analyses based on Bloch–Floquet theory. The acoustic band structures of the PAMs were calculated with periodic boundaries. Tunable bandgaps in certain frequency ranges were generated by various deformations applied to the PAMs. Wave attenuation in experimental transmission loss at specific frequencies was demonstrated, showing favorable agreement with the bandgaps obtained from numerical calculations. Both the numerical and experimental results indicate that the proposed structure demonstrates great tunability and offers significant advantages over the regular materials for controlling sound wave propagation and filtering sound waves within specific frequency ranges.

A combination tillage with disks, rippers, and roller baskets allows the loosening of compacted soils and the crumbling of soil clods. Statistical methods for evaluating the soil tilth quality of combination tillage are limited. Light Detection and Ranging (LiDAR) data and machine learning models (Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)) are proposed to investigate roller basket pressure settings on soil tilth quality. Soil profiles were measured using LiDAR (stop and go and on-the-go) and RGB visual images from a Completely Randomized Design (CRD) tillage experiment on clay loam soil with treatments of roller basket down, roller basket up, and no-till in three replicates. Utilizing RF, SVM, and NN methods on the LiDAR data set identified median, mean, maximum, and standard deviation as the top features of importance variables that were statistically affected by the roller settings. Applying multivariate discriminatory analysis on the four statistical measures, three soil tilth classes were predicted with mean prediction rates of 77% (Roller-basket down), 64% (Roller-basket up), and 90% (No till). The LiDAR data analytics-inspired soil tilth classes correlated well with the RGB image discriminatory analysis. Soil tilth machine learning models were shown to be successful in classifying soil tilth with regard to onboard operator pressure control settings on the roller basket of the combination tillage implement.

Modal parameter monitoring is a widely used structural health monitoring method. However, among other limitations, this method cannot effectively identify slight damage under ambient conditions. This study proposed a novel strain expansion–reduction approach for identifying damage. To verify the feasibility of the proposed method, we numerically and experimentally tested the method using a rigid acrylic frame. The frame was artificially damaged at various depths to reflect various damage scenarios. The increase in the damage index provided an accurate estimation of damage severity. For the case with merely 0.5% damage zone in one slat, the index is increased by 259% of the intact case. When the damage zone was doubled, the index increases significantly by 467% of the intact case, demonstrating excellent sensitivity of the proposed method. To guarantee practical use, the numerical model of the proposed method was applied to an offshore wind turbine jacket substructure and successfully identified multiple damage sites and the damage severity with extremely high (>10) damage index.