An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot

Corrosion is one of the main causes of deterioration of steel bridges. It may cause metal loss and fatigue cracks in the steel components, which would lead to the collapse of steel bridges. This paper presents an automated sensing system to detect corrosion, crack, and other kinds of defects using a GMR (Giant Magnetoresistance) sensor array. Defects will change the relative permeability and electrical conductivity of the material. As a result, magnetic field density generated by ferromagnetic material and the magnetic wheels will be changed. The defects are able to be detected by using GMR sensor array to measure the changes of magnetic flux density. In this study, magnetic wheels are used not only as the adhesion device of the robot, but also as an excitation source to provide the exciting magnetic field for the sensing system. Furthermore, compared to the eddy current method and the MFL (magnetic flux leakage) method, this sensing system suppresses the noise from lift-off value fluctuation by measuring the vertical component of induced magnetic field that is perpendicular to the surface of the specimen in the corrosion inspection. Simulations and experimental results validated the feasibility of the system for the automated defect inspection

Web-GIS Based Visualization System of Predicted Ground Vibration Induced by Blasting in Urban Quarry Sites

Blasting is routinely carried out at various resource extraction sites, even in urban areas. As a consequence of this, residents around urban quarry sites are affected by ground vibration induced by blasting on a regular basis. In this study, a prediction and visualization system for ground vibrations is developed for the purpose of reducing the adverse psychological effects of blasting. The system consists of predicting ground vibration using an Artificial Neural Network (ANN) and visualizing it on an online map using Web-GIS. A prediction model using ANN that learned the optimum weight by taking 50 sets of data indicated a regression value of 0.859 and a Mean Square Error (MSE) of 0.0228. Compared with previous researches, these values are not bad results. Peak Particle Velocity (PPV) was used as a metric to measure ground vibration intensity. A color contour is generated using GIS tools based on the PPV value of each prediction point. The system is completed by overlaying the contour onto a basic map in a website. The basic map shows the surrounding area through the use of Google Maps data. This system can be used by anyone with access to the internet and a browser, requiring no special software or hardware. In addition, mining operations can utilize the data to modify blasting design and planning to minimize ground vibration. In conclusion, this system has the potential to alleviate the worries of surrounding residents caused by ground vibrations from blasting due to the fact that they can personally check the predicted vibration around their locale. Furthermore, since this data will be publicly available on the internet, it is also possible that this system can contribute to research in other fields

University Students’ Preferences for Labour Conditions at a Mining Site: Evidence from Two Australian Universities

The mining industry makes up a large portion of the gross domestic product (GDP) in Australia, although securing human resources remains a problem in that field. The aim of this paper is to identify Australian university mining students’ preferences, considering it as potential employees’ preferences, for labour conditions at mining sites by means of a discrete choice experiment to promote efficient improvements in labour conditions in the mining industry. The data of 93 respondents analysed in this paper was collected by survey carried out in two universities in Australia. The result of the study showed that students have preferences on several factors such as wage, fatality rate, working position, commuting style, and company. Students having specific sociodemographic characters were found to show specific preferences on labour conditions. The results of this study indicate the potential average of appropriate monetary compensation for each factor

Update of HΦ\mathcal{H}\Phi: Newly added functions and methods in versions 2 and 3

$\mathcal{H}\Phi$ [$aitch$-$phi$] is an open-source software package of numerically exact and stochastic calculations for a wide range of quantum many-body systems. In this paper, we present the newly added functions and the implemented methods in vers. 2 and 3. In ver. 2, we implement spectrum calculations by the shifted Krylov method, and low-energy excited state calculations by the locally optimal blocking preconditioned conjugate gradient (LOBPCG) method. In ver. 3, we implement the full diagonalization method using ScaLAPACK and GPGPU computing via MAGMA. We also implement a real-time evolution method and the canonical thermal pure quantum (cTPQ) state method for finite-temperature calculations. The Wannier90 format for specifying the Hamiltonians is also implemented. Using the Wannier90 format, it is possible to perform the calculations for the $ab$ $initio$ low-energy effective Hamiltonians of solids obtained by the open-source software RESPACK. We also update Standard mode \unicode{x2014}simplified input format in $\mathcal{H}\Phi$\unicode{x2014} to use these functions and methods. We explain the basics of the implemented methods and how to use them.Comment: 21 pages, 10 figures, 2 table

One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction

An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% R-2, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s

Kω — Open-source library for the shifted Krylov subspace method of the form (zI−H)x=b

We develop Kω, an open-source linear algebra library for the shifted Krylov subspace methods. The methods solve a set of shifted linear equations (zkI−H)x(k)=b(k=0,1,2,…) for a given matrix H and a vector b, simultaneously. The leading order of the operational cost is the same as that for a single equation. The shift invariance of the Krylov subspace is the mathematical foundation of the shifted Krylov subspace methods. Applications in materials science are presented to demonstrate the advantages of the algorithm over the standard Krylov subspace methods such as the Lanczos method. We introduce benchmark calculations of (i) an excited (optical) spectrum and (ii) intermediate eigenvalues by the contour integral on the complex plane. In combination with the quantum lattice solver HΦ, Kω can realize parallel computation of excitation spectra and intermediate eigenvalues for various quantum lattice models