Deflection is one of the key indexes for the safety evaluation of bridge structures. In reality, due to the changing operational and environmental conditions, the deflection signals measured by structural health monitoring systems are greatly affected. These ambient changes in the system often cover subtle changes in the vibration signals caused by damage to the system. The deflection signals of prestressed concrete (PC) bridges are regarded as the superposition of different effects, including concrete shrinkage, creep, prestress loss, material deterioration, temperature effects, and live load effects. According to multiscale analysis theory of the long-term deflection signal, in this paper, an integrated machine learning algorithm that combines a Butterworth filter, ensemble empirical mode decomposition (EEMD), principle component analysis (PCA), and fast independent component analysis (FastICA) is proposed for separating the individual deflection components from a measured single channel deflection signal. The proposed algorithm consists of four stages: (1) the live load effect, which is a high-frequency signal, is separated from the raw signal by a Butterworth filter; (2) the EEMD algorithm is used to extract the intrinsic mode function (IMF) components; (3) these IMFs are utilized as input in the PCA model and some uncorrelated and dominant basis components are extracted; and (4) FastICA is applied to derive the independent deflection component. The simulated results show that each individual deflection component can be successfully separated when the noise level is under 10%. Verified by a practical application, the algorithm is feasible for extracting the structural deflection (including concrete shrinkage, creep, and prestress loss) only caused by structural damage or material deterioration.
In this work, nano ZSM-5 zeolite with superior catalytic properties was synthesized from laponite as part of Si source by dry gel conversion (DGC) method, and its crystallization mechanism was...
ZSM-5/SAPO-34 zeolite composites were successfully synthesized by adding ZSM-5 into SAPO-34 synthetic gel under hydrothermal synthesis conditions, and the SAPO-34 as a part of composite zeolite was synthesized from laponite...
In this work, SAPO‐34 zeolite with high catalytic activity was successfully synthesized by hydrothermal method from laponite as the single Si source. Compared with the traditional method of synthesizing zeolites from clay, the laponite after swelling can be directly used in the synthesis process of zeolite. The crystallization behavior of SAPO‐34 synthesized from laponite was investigated by XRD, FTIR, N2 adsorption–desorption, SEM‐EDX, ICP‐MS, and DR‐UV/Vis. The process can be summarized as follows: the Si and Mg elements were produced by the depolymerization of laponite, then the primary building units of SAPO‐34 zeolite were formed under hydrothermal conditions; finally, the crystal nucleus began to form, and crystallization was triggered. Compared with the conventional SAPO‐34 synthesized from TEOS, the SAPO‐34 synthesized from laponite exhibited higher selectivity to light olefins and longer lifetime, which can be attributed to the suitable strength of acidity. This work points to a straightforward route to the synthesis of SAPO‐34 from laponite.
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