Unconfined compressive strength and failure behaviour of completely weathered granite from a fault zone

Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.

A smart data-driven rapid method to recognize the strawberry maturity

In recent years,there have been many studies on the recognition of strawberry maturity,but there are still problems such as low recognition accuracy and expensive experimental instruments.These factors make their methods difficult for farmers to use.To solve these problems,we developed a fast,non-destructive,accurate and convenient method for strawberry maturity identification using smartphones.In this paper,strawberry maturity is divided into three levels:mature,nearly-mature and immature.Considering the actual strawberry harvest process and postharvest handling,we focus on the differentiation between the mature and the nearly-mature ones to help farmers reduce possible damage in transit and improve profitability.We obtained the images of strawberries with different maturities at 535 nm and 670 nm wavelengths through a smartphone and got absorbance data by image processing based on the region of interest.The absorbance data were used to establish three maturity recognition models—i.e.,multivariate linear,multivariate nonlinear and SoftMax regression classifier.The results showed that the multivariate nonlinear model had the highest identification accuracy(which is over 94%)in the greenhouse.Therefore,this method has considerable potential as a means for rapid recognition of strawberry maturity.