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Typically, material modeling has involved the development of mathematical models of material behavior derived from human observation of experimental data. An alternative procedure, discussed in this paper, is to use a computation and knowledge representation paradigm, called a network, to model material behavior. The main benefits in using a neural network is that the network is built directly from experimental data using the self-organizing capabilities of the neural network, i.e., the network is presented with the experimental data and learns the relationships between stresses and strains. Such a modeling strategy has important implications for modeling the behavior of complex materials. In this paper, the stress-strain relationship of confined concrete in hollow bridge columns is modeled with a back-propagation neural network. The results of using networks to study the behavior of confined concrete look very promising.
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Self-sensing material is one of the most important components of smart construction. As a promising stress self-sensing material, carbon nanotube (CNT)/cement composite has been widely studied in the past decade. The stress self-sensing performance, which is reflected by the piezoresistivity of the CNT/cement composite, can be determined by several factors, such as CNT dispersion, water/binder ratio, or loading directions. Although these factors have been systematically investigated to demonstrate their effects on the self-sensing performance of CNT/cement composite, the variation of the percolation networks of CNT in the cement matrix, which is another important factor that determines the piezoresistivity of the CNT/cement composite, was barely discussed before. In this study, the variation of the CNT percolation network in cement matrix under compression loading was calculated based on the percolation theory; and the piezoresistivity of the CNT/cement composite below and above the percolation threshold was analyzed from the perspective of the effective percolation networks of CNT in the CNT/cement composite. Furthermore, the mechanism of the piezoresistivity variation was elucidated via calculating the percolation backbone density. This study not only gives a basic introduction to calculate the effective percolation networks of CNT in the cement matrix, but also shed light on how to obtain a CNT/cement composite with a stable stress self-sensing performance.
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Abstract: The Qaidam basin in W China is an immense hyperarid intramontane basin with flat vast playas and salt lakes on the Qinghai-Tibet Plateau. The central basin is about 2800-2900 m a.s.l. elevation and enclosed by mountain ranges reaching > 5800 m in the Qilian Mountains and > 6200 m in the eastern Kunlun Mountains. The extensive playas of the basin are covered by gypsum or halite with very subordinate additional solids. In this contribution we report on the chemical composition of salt lakes and inflows to the Qaidam basin (analysis of 30 water samples collected in the summer of 2008 and 2009) together with the composition of 22 salt samples. Salt lakes and small salt ponds formed at topographic depressions. Some of the lakes cover > 300 km2 surface but are very shallow (1-2 m deep). Most salt lakes and salt ponds are NaCl dominated and contain typically 250-300 g kg−1 total dissolved solids (TDS). Some lakes are industrially used and produce KCl fertilizer, LiCl, and boron or are strongly modified by deep water produced in oil fields. Lakes along the borders to the high mountains are typically not fully saturated with halite. However, also these lakes lost most Ca and are drastically enriched in Mg and some lakes also in B and Li. The chemical development of the most natural salt lakes follows a path producing Ca-deficient water that ultimately precipitate Mg-bearing carbonates and chlorites in addition to halite upon evaporation. The salt lakes form by continuous and drastic evaporation of the waters supplied by the inflows to the lakes in the basin. All inflows carry considerable amounts of Cl and are characterized by very high Cl/Br ratios. These chemical characteristics suggest that the salt load of the inflows originates mostly from re-dissolved windblown halite deposited together with sand up to high altitudes in the bordering mountain ranges. Also, thermal waters ascending along deep faults along the Qilian Mountains carry considerable amounts of chloride. Their low Cl/Br ratio however suggests that most of the dissolved Na is derived from minerals of the basement rocks by fluid-rock interaction at T > 130 °C. The thermal fluids also carry considerable amounts of boron, indicating that co-precipitated borax in the salt lakes ultimately also derives from minerals in the basement rocks (tourmaline). Consequently, the presented data improve the understanding how the brines and salt lake waters develop from a wide range of chemically distinct low-TDS inflows and how the sequences of minerals precipitated upon evaporation in the Qaidam basin formed
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· 2019
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