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Experimental study on mechanical properties of nano-modified cement paste

Wang Zhi, Qin Wenjing, Zhang Lijuan

Archives of Civil Engineering

2020

Vol. 66, nr 3

253--263

The mechanical properties of cement paste modified by nano-TiO2 (nT) and nano-SiO2 (nS) were experimentally studied. The compressive strength increased first and then decreased with the increase of nanoparticle content. When nanoparticles were added into the cement paste as a filler to improve the microstructure, the two kinds of particles both could form a tighter mesh structure, which would enhance the density and strength of the structure. The elastic modulus increased rapidly with the increase of the nT content and reached a peak when the nanoparticle content is about 3%, which was about twice the elastic modulus of ordinary cement paste. The Scanning electron microscopy (SEM) observation results showed that the microstructure of cement was network connection and fiber tube. The hydration progress of ordinary cement slurry was insufficient, and many unreacted cement particles remained. With the addition of nanoparticles, the internal structure of the cement became denser, with fewer pore cracks, smaller pore diameters, more complex fiber tube arrangements, and significant anisotropy, thereby improving strength and mechanical properties.

Principal Slip Zone determination in the Wenchuan earthquake Fault Scientifc Drilling project hole 1: considering the Bayesian discriminant function

Fang Sinan, Zhang Zhansong, Wang Zhi, Pan Heping, Du Ting

Acta Geophysica

2020

Vol. 68, no. 6

1595--1607

Accurate determination of the Principal Slip Zone (PSZ) of earthquake fault zones is a key task of earthquake Fault Scientifc Drilling for future earthquake control. The fault zone structure of Wenchuan earthquake is complex, and there are many strong earthquakes recorded on the fault zone, which make determining the PSZ in the Wenchuan earthquake Fault Scientifc Drilling project-hole 1 (WFSD-1) difcult. At present, core analysis of whole coring is the decisive method for determining PSZ depth, and the fresh fault gouge at 589.2 m is the PSZ in WFSD-1. Abundant and comprehensive logging data can only be used as evidence to judge the PSZ. Based on the discrimination function and hyperplane equation in Bayes ian discriminant classifcation, we derive a new algorithm for computing the PSZ possibility using a Bayesian Discrimina tion function (PSZP-BDF) based on the simplifed model, and set up a mode to determine the PSZ directly using machine learning of well logging. For the verifcation of WFSD-1, the fault gouges are successfully identifed and the PSZ depth is accurately located. The algorithm objectively learns the sample data, which is naturally adaptive to the region. The calculation procedure is simple and does not require expensive coring data or heavy core tests in the well. The calculation speed is fast, using multiple physical data types. The PSZP-BDF algorithm is suitable for processing and interpreting earthquake fault scientifc drilling data.