Feasibility in Estimating the Dry Leaf Mass and Specific Leaf Area of 50 Bamboo Species Based on Nondestructive Measurements

Specific leaf area (SLA) is a good predictor of aboveground net primary productivity. However, the SLA of bamboo species is generally estimated on the basis of destructive measurements rather than the cost-effective and recyclable nondestructive measurements using easily accessible leaf traits such as leaf length (L) and width (W). Considering the strong empirical relationships between leaf area (LA) and leaf structural parameters of bamboo species that were developed by previous studies, this study explores the feasibility of estimating the leaf dry mass (LDM) and SLA of 50 bamboo species using L and W. The results show that the Montgomery equation and its similar forms precisely estimated LA of the 50 bamboo species at both leaf scale (R2 > 0.96 and MAE% < 4.67%) and the canopy scale (R2 > 0.99 and RMSE < 0.09); the LDM of the 50 bamboo species could also be estimated using L and W at both leaf scale (R2 > 0.52 and MAE% < 26.35%) and the canopy scale (R2 > 0.99 and RMSE < 0.003), and the estimated mean SLA of each of the 50 bamboo species had good agreement with the measured values (R2 > 0.99 and RMSE < 1.88) because of the precisely estimated mean LA and mean LDM at the canopy scale, indicating the feasibility of estimating SLA of the 50 bamboo species at the canopy scale based on nondestructive measurements. However, the empirical relationships used for mean SLA estimations are not suitable for SLA estimations at the leaf scale because of the uncertainties in the estimated LDM at the leaf scale.

Size/Age Models for Monitoring of the Pink Sea Fan Eunicella verrucosa (Cnidaria: Alcyonacea) and a Case Study Application

The pink sea fan Eunicella verrucosa is a habitat-forming octocoral living in the East Atlantic and in the Mediterranean Sea where, under proper circumstances, it can form large populations known as coral forests. Although these coral forests represent vulnerable marine ecosystems of great importance, these habitats are still poorly known, and their monitoring is almost non-existent to date. For this reason, we compared two different models to infer the age of E. verrucosa based on nondestructive measurements of the colonies’ size, in order to highlight strengths and weaknesses of the existing tools for a potential application in long-term monitoring. We also applied the two models on a case-study population recently found in the northwest Mediterranean Sea. Our results showed which model was more reliable from a biological point of view, considering both its structure and the results obtained on the case study. However, this model uses solely the height of the colonies as proxy to infer the age, while the total branch fan surface area could represent a more appropriate biometric parameter to monitor the size and the growth of E. verrucosa.

Bayesian Information Fusion of Multmodality Nondestructive Measurements for Probabilistic Mechanical Property Estimation

Missing data occur when no data value is available for the variable in an observation. In this research, Bayesian data augmentation method is adopted and implemented for prediction with missing data. The data augmentation process is conducted through Bayesian inference with missing data assuming the multivariate normal distribution. Gibbs sampling is used to draw posterior simulations of the joint distribution of unknown parameters and unobserved quantities. The missing elements of the data are sampled conditional on the observed elements. The distribution of model parameters and variables with missing data can be obtained for reliability analysis. Two examples are given to illustrate the engineering application of Bayesian inference with missing data. The first example is to predict the yield strength of aging pipeline by fusing the incomplete surface information with missing data. The predictive performance is compared among direct surface indentation technique, linear regression with complete data and Bayesian inference with missing data. The second example is to predict the fatigue life of corroded steel reinforcing bar from the incomplete input dataset. The predicted fatigue lives are compared with experimental data. Both examples demonstrate that the Bayesian method can deal with missing data problem properly and show good predictive performance.

Aging Mechanisms and Monitoring of Cable Polymers

Aging mechanisms of two polymeric insulation materials that are used widely in nuclear power plant low-voltage cables; cross-linked polyethylene (XLPE) and ethylene propylene rubber/ethylene propylene diene terpolymer (EPR/EPDM), are reviewed. A summary of various nondestructive methods suitable for evaluation of cable insulation is given. A capacitive sensor capable of making local nondestructive measurements of capacitance and dissipation factor on cable polymers, and potentially suitable for in situ cable monitoring, is introduced.Correlating values of elongation-at-break, indenter modulus, capacitance and dissipation factor measured on a set of 47 aged flame-resistant EPR samples shows a higher correlation between indenter modulus and dissipation factor than between indenter modulus and elongation-at-break.

Imaging Fatigue Damage Precursors Based on Nonlinear Phased Array Ultrasonic Measurements of Diffuse Field

Nonlinear ultrasonic (NLU) techniques have emerged as a potential solution to improve the resolution of nondestructive measurements to detect microstructural changes of cyclically loaded materials. However, current NLU methods need power-demanding instrumentation that is useful only in the laboratory settings. On the other hand, phased array systems provide the capability of sensing such changes when the later portion of the elastic waveforms, called diffuse field, is analyzed. Moreover, phased array systems are an excellent solution for field test measurement and imaging of material damage. This study explores the use of NLU metrics based on ratios of harmonic amplitudes and frequencies to map the buildup of damage precursors, such as crystal dislocations, under cyclic loading within the microstructure of fatigued 2024-T3 aluminum specimens. The results show that these metrics are highly sensitive to microstructural fatigue damage making them significantly important to measure mechanical properties, such as fracture toughness, that are extremely useful in predicting the remaining useful life of a studied material. A nonlinear metric of elastic energy that encapsulates the nonlinear effects of subharmonic and higher-harmonic generations and frequency ratio is proposed. These effects of spectral energy shifts are combined making this metric highly sensitive to nano- and micro-scale damage within the fatigued medium.