Quantitative Detection of Corrosion State of Concrete Internal Reinforcement Based on Metal Magnetic Memory

Corrosion can be very harmful to the service life and several properties of reinforced concrete structures.The metal magnetic memory(MMM)method,as a newly developed spontaneous magnetic flux leakage(SMFL)non-destructive testing(NDT)technique,is considered a potentially viable method for detecting corrosion damage in reinforced concrete members.To this end,in this paper,the indoor electrochemical method was employed to accelerate the corrosion of outsourced concrete specimens with different steel bar diameters,and the normal components Bz and its gradient of the SMFL fields on the specimen surfaces were investigated based on the metal magnetic memory(MMM)method.The experimental results showed that the SMFL experimental Bz curves are consistent with the analytical results of the theoretical model.Furthermore,the crest-to-trough behavior on the Bz signal curve and its zero-point gradient spacing can more accurately indicate the corroded area’s extent.Then,a magnetic characteristic parameter W based on a large amount of experimental data was established to characterize the degree of corrosion of the steel bars.The magnetic characteristic parameter W is linearly related to the maximum cross-sectional area loss rate
of the corroded reinforcement.This paper will lay the foundation for future research on corrosion detection of reinforced concrete structures based on the MMM method and provide a feasible way for non-destructive detection of corrosion independent of the influence of reinforcement diameter and magnetization history.

Investigation of granite failure precursor under axial load using modified LSTM framework

Granite is usually composed of quartz,biotite,feldspar,and cracks,and the variation characteristics of these components could reflect the deformation and failure process of rock well.Taking granite as an example,the video camera was used to record the deformation and failure process of rock.The distribution of meso-components in video images was then identified.The meso-components of rock failure precursors were also discussed.Moreover,a modified LSTM(long short-term memory method)based on SSA(sparrow search algorithm)was proposed to estimate the change of meso-components of rock failure precursor.It shows that the initiation and expansion of cracks are mainly caused by feldspar and quartz fracture,and when the quartz and feldspar exit the stress framework,rock failure occurs;the second large increase of crack area and the second large decrease of quartz or feldspar area may be used as a precursor of rock failure;the precursor time of rock failure based on meso-scopic components is about 4 s earlier than that observed by the naked eye;the modified LSTM network has the strongest estimation ability for quartz area change,followed by feldspar and biotite,and has the worst estimation ability for cracks;when using the modified LSTM network to predict the precursors of rock instability and failure,quartz and feldspar could be given priority.The results presented herein may provide reference in the investigation of rock failure mechanism.

LIMITED MEMORY BFGS METHOD BY USING LINEAR INDEPENDENT SEARCH DIRECTIONS

The degree of numerical linear independence is proposed and discussed. Based on this linear independence theory, a modified limited memory BFGS method is deve loped. Similar to the standard limited memory method, this new method determines the new update by applying the updating formula m times to an initial positive diagonal matrix using the m previous pairs of the change in iteration and gradient. Besides the most recent pair of the change, which guarantees the quadratic termination, the choice of the other ( m -1) pairs of the change in the new method is dependent on the degree of numerical linear independence of previous search directions. In addition, the numerical linear independence theory is further discussed and the computation of the degree of linear independence is simplified. Theoretical and numerical results show that this new modified method improves efficiently the standard limited memory method.

GLOBAL CONVERGENCE RESULTS OF A THREE TERM MEMORY GRADIENT METHOD WITH A NON-MONOTONE LINE SEARCH TECHNIQUE

In this paper, a new class of three term memory gradient method with non-monotone line search technique for unconstrained optimization is presented. Global convergence properties of the new methods are discussed. Combining the quasi-Newton method with the new method, the former is modified to have global convergence property. Numerical results show that the new algorithm is efficient.

Feasibility Research on Fatigue Damage Evaluation Based on Magnetic Memory Method

To confirm whether Magnetic Memory Method can be used to evaluate the fatigue damage of ferromagnetic part or not,this paper stated the principle of magnetic memory method.Also, the relationship between testing parameter of magnetic memory method and fatigue damage was analyzed based on the experiment. The results show that magnetic memory method can be used to evaluate the fatigue damage represented by stress concentration. However, the relationship between the parameter of magnetic memory method and fatigue damage still needs to be studied.

Superelasticity of Cu–Ni–Al shape-memory fibers prepared by melt extraction technique

In the paper, a melt extraction method was used to fabricate Cu–4Ni–14Al（wt%） fiber materials with diameters between 50 and 200 μm. The fibers exhibited superelasticity and temperature-induced martensitic transformation. The microstructures and superelasticity behavior of the fibers were studied via scanning electron microscopy（SEM） and a dynamic mechanical analyzer（DMA）, respectively. Appropriate heat treatment further improves the plasticity of Cu-based alloys. The serration behavior observed during the loading process is due to the multiple martensite phase transformation.

Set Programming Method and Performance Improvement of Phase Change Random Access Memory Arrays

A novel slow-down set waveform is proposed to improve the set performance and a 1 kb phase change random access memory chip fabricated with a 13nm CMOS technology is implemented to investigate the set performance by different set programming strategies based on this new set pulse. The amplitude difference （I1 - I2） of the set pulse is proved to be a crucial parameter for set programming. We observe and analyze the cell characteristics with different I1 - I2 by means of thermal simulations and high-resolution transmission electron microscopy, which reveal that an incomplete set programming will occur when the proposed slow-down pulse is set with an improperly high I1 - I2. This will lead to an amorphous residue in the active region. We also discuss the programming method to avoid the set performance degradations.

Improved hybrid iterative optimization method for seismic full waveform inversion

In full waveform inversion （FWI）, Hessian information of the misfit function is of vital importance for accelerating the convergence of the inversion; however, it usually is not feasible to directly calculate the Hessian matrix and its inverse. Although the limited memory Broyden-Fletcher-Goldfarb-Shanno （L-BFGS） or Hessian-free inexact Newton （HFN） methods are able to use approximate Hessian information, the information they collect is limited. The two methods can be interlaced because they are able to provide Hessian information for each other; however, the performance of the hybrid iterative method is dependent on the effective switch between the two methods. We have designed a new scheme to realize the dynamic switch between the two methods based on the decrease ratio （DR） of the misfit function （objective function）, and we propose a modified hybrid iterative optimization method. In the new scheme, we compare the DR of the two methods for a given computational cost, and choose the method with a faster DR. Using these steps, the modified method always implements the most efficient method. The results of Marmousi and overthrust model testings indicate that the convergence with our modified method is significantly faster than that in the L-BFGS method with no loss of inversion quality. Moreover, our modified outperforms the enriched method by a little speedup of the convergence. It also exhibits better efficiency than the HFN method.

A self-adaptive,data-driven method to predict the cycling life of lithium-ion batteries

Accurately forecasting the nonlinear degradation of lithium-ion batteries(LIBs)using early-cycle data can obviously shorten the battery test time,which accelerates battery optimization and production.In this work,a self-adaptive long short-term memory(SA-LSTM)method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data.Specifically,two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model.The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model.The proposed method achieved an average online prediction error of 6.00%and 6.74%for discharge capacity and end of life,respectively,when using the early-cycle discharge information until 90%capacity retention.Fur-thermore,the importance of temperature control was highlighted by correlat-ing the features with the average temperature in each cycle.This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs,and unveils the underlying degradation mechanism and the impor-tance of controlling environmental temperature.

A NEW DESCENT MEMORY GRADIENT METHOD AND ITS GLOBAL CONVERGENCE

In this article, a new descent memory gradient method without restarts is proposed for solving large scale unconstrained optimization problems. The method has the following attractive properties： 1） The search direction is always a sufficiently descent direction at every iteration without the line search used; 2） The search direction always satisfies the angle property, which is independent of the convexity of the objective function. Under mild conditions, the authors prove that the proposed method has global convergence, and its convergence rate is also investigated. The numerical results show that the new descent memory method is efficient for the given test problems.

LIMITED MEMORY BFGS METHOD FOR NONLINEAR MONOTONE EQUATIONS

In this paper, we propose an algorithm for solving nonlinear monotone equations by combining the limited memory BFGS method （L-BFGS） with a projection method. We show that the method is globally convergent if the equation involves a Lipschitz continuous monotone function. We also present some preliminary numerical results.

Global Convergence of a Modified Limited Memory BFGS Method for Non-convex Minimization

In this paper, a modified limited memory BFGS method for solving large-scale unconstrained optimization problems is proposed. A remarkable feature of the proposed method is that it possesses global convergence property without convexity assumption on the objective function. Under some suitable conditions, the global convergence of the proposed method is proved. Some numerical results are reported which illustrate that the proposed method is efficient.

LIMITED MEMORY QUASI-NEWTON METHOD FOR LARGE-SCALE LINEARLY EQUALITY-CONSTRAINED MINIMIZATION

In this paper, a new limited memory quasi-Newton method is proposed and developed for solving large-scale linearly equality-constrained nonlinear programming problems. In every iteration, a linear equation subproblem is solved by using the scaled conjugate gradient method. A truncated solution of the subproblem is determined so that computation is decreased. The technique of limited memory is used to update the approximated inverse Hessian matrix of the Lagrangian function. Hence, the new method is able to handle large dense problems. The convergence of the method is analyzed and numerical results are reported.