RESUMEN
The vibration generated during the construction of subway tunnels with double-shield tunnel boring machine (TBM) has a significant impact on the environment, which has caused multiple complaints from residents. Taking a double-shield TBM tunnel project as the background, vibration measurements were conducted by installing vibration sensors on-site. By combining theoretical methods-such as normalization, polynomial fitting prediction, and gray correlation analysis-the vibration characteristics, impact range on the environment, and factors affecting the vibration of TBM construction were studied. The key research results included: (1) The amplified zone of X and Y vibration acceleration occurred on the left-hand side of the tunnel from 3.15 to 13.85 m, but rapidly decayed away from the amplification zone. (2) The impact range of TBM vibrations on residential areas at night and during the day was studied according to the official "Urban Regional Environmental Vibration Standard" and it was found to be larger at night than during the day. (3)The main factors affecting the TBM vibration level was studied-including the cutter-head torque, TBM thrust, cutter-head speed, penetration, field penetration index (FPI) and so on. In summary, when the double-shield TBM construction tunnel is adjacent to residential areas, the vibration generated exceeds the national standard limit. In order to reduce the impact of TBM vibration on residential areas, excavation parameters such as cutter head torque, TBM thrust, cutter head speed, and penetration should be appropriately reduced.
RESUMEN
BACKGROUND: Volatile organic compounds (VOCs) in grain fluctuate depending on the degree of grain freshness. A new colorimetric sensor array (CSA) was developed as capture probes for the quantification of VOCs in grains in this work, and it was designed to monitor the variation of grain VOCs. CSA spectral data acquisition using visible-near-infrared spectroscopy and image processing of CSA's image imformation by computer were used comparatively. Then, machine-learning-based models - for example, synergistic interval partial least squares, genetic algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant colony optimization (ACO) algorithm - were introduced to optimize variables. Moreover, principal component analysis, and linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were used for the classification. Ultimately, quantitative models for detecting grain freshness are developed using various variable selection strategies. RESULTS: Compared with the pattern recognition results of image processing, visible-near-infrared spectroscopy could better separate the grains with different freshness from principal component analysis, and the prediction set of LDA models could correctly identify 100% of rice, 96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, the LDA model and KNN model based on genetic algorithms show the best prediction performance. The prediction set could correctly identify 100% of rice and paddy samples and 95.83% of soybean samples. CONCLUSION: The method developed could be used for non-destructive detection of grain freshness. © 2023 Society of Chemical Industry.