Review of Reactor Neutrino Oscillation Experiments

In this document we will review the current status of reactor neutrino oscillation experiments and present their physics potentials for measuring the $\theta_{13}$ neutrino mixing angle. The neutrino mixing angle $\theta_{13}$ is currently a high-priority topic in the field of neutrino physics. There are currently three different reactor neutrino experiments, \textsc{Double Chooz}, \textsc{Daya Bay} and \textsc{Reno} and a few accelerator neutrino experiments searching for neutrino oscillations induced by this angle. A description of the reactor experiments searching for a non-zero value of $\theta_{13}$ is given, along with a discussion of the sensitivities that these experiments can reach in the near future.Comment: 15 pages, 4 figure

A finite element flux-corrected transport method for wave propagation in heterogeneous solids

When moving discontinuities in solids need to be simulated, standard finite element (FE) procedures usually attain low accuracy because of spurious oscillations appearing behind the discontinuity fronts. To assure an accurate tracking of traveling stress waves in heterogeneous media, we propose here a flux-corrected transport (FCT) technique for structured as well as unstructured space discretizations. The FCT technique consists of post-processing the FE velocity field via diffusive/antidiffusive fluxes, which rely upon an algorithmic length-scale parameter. To study the behavior of heterogeneous bodies featuring compliant interphases of any shape, a general scheme for computing diffusive/antidiffusive fluxes close to phase boundaries is proposed too. The performance of the new FE-FCT method is assessed through one-dimensional and two-dimensional simulations of dilatational stress waves propagating along homogeneous and composite rods

Digitalization of Supply Chain and Its Impact on Cost, Firm Performance, and Resilience: Technology Turbulence and Top Management Commitment as Moderator

This article determines the impact of supply chain digitalization (SCD) on firm performance and resilience. We also investigate the moderating role of technology turbulence (TT) and top management commitment (MC). A theoretical model is developed from the inputs from literature review and resource-based view, dynamic capability view, and absorptive capacity theories. The theoretical model is then validated using structural equation modeling with consideration of 712 usable responses from different service and manufacturing firms. Multigroup analysis was also conducted to investigate the moderating role of TT and top MC. The article finds that SCD has a significant impact on the cost performance of the firms, which in turn impacts significantly and positively on firm performance, mediated through operational performance of the firms. The article also highlights that there is a considerable moderating impact of TT and top MC on the digitalization of the supply chain management process

A Multi-stage Machine Learning Methodology for Health Monitoring of Largely Unobserved Structures Under Varying Environmental Conditions

Structural Health Monitoring (SHM) via data-driven techniques can be based upon vibrations acquired by sensor networks. However, technical and economic reasons may prevent the deployment of pervasive sensor networks over civil structures, thus limiting their reliability in terms of damage detection. Moreover, the effects of environmental (and operational) variability may lead to false alarms. To address these challenges, a multi-stage machine learning (ML) method is here proposed by exploiting autoregressive (AR) spectra as damage-sensitive features. The proposed method is framed as follows: (i) computing the distances between different sets of the AR spectra via the log-spectral distance (LSD), providing also the training and test datasets; (ii) removing the potential environmental variability by an auto-associative artificial neural network (AANN), to set normalized training and test datasets; (iii) running a statistical analysis via the Mahalanobis-squared distance (MSD) for early damage detection. The effectiveness of the proposed approach is assessed in the case of limited vibration data for the laboratory truss structure known as the Wooden Bridge. Comparative studies show that the AR spectrum is a reliable feature, sensitive to damage even in the presence of a limited number of sensors in the network; additionally, the multi-stage ML methodology succeeds in early detecting damage under environmental variability

A Multi-Fidelity Deep Neural Network Approach to Structural Health Monitoring

The structural health monitoring (SHM) of civil structures and infrastructures is becoming a crucial issue in our smart and hyper-connected age. Due to structural aging and to unexpected loading conditions, partially linked to extreme events caused by the climate change, reliable and real-time SHM schemes are currently facing a burst in development and applications. In this work, we propose a procedure that relies upon a surrogate modeling scheme based on a multi-fidelity (MF) deep neural network (DNN), which has been conceived to sense and identify a structural damage under operational (and possibly environmental) variability. By exploiting the sensor recordings from a densely deployed network within a fully stochastic framework, the MF-DNN model is adopted to feed a Markov chain Monte Carlo (MCMC) sampling procedure and update the probability distribution of the structural state, conditioned on noisy observations. As information regarding the health of real structures is usually rather limited, the datasets to train the MF-DNN are generated with physical (e.g., finite element) models: high-fidelity (HF) and low-fidelity (LF) models are adopted to simulate the structural response under the mentioned varying conditions, respectively, in the presence or absence of a structural damage. As far as the architecture of the DNN is concerned, the MF approach is obtained by merging a fully connected LF-DNN and a long short-term memory HF-DNN. The LF-DNN mimics the output of the sensor network in the undamaged condition, while the HF-DNN is exploited to improve the LF model and appropriately catch the structural response in the presence of a pre-defined set of damaged patterns. Thanks to the adaptive enrichment of the LF signals carried out by the MF-DNN, the proposed model updating strategy is reported capable of locating (and possibly quantifying) a damage event

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen-Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies

Classification of the Structural Behavior of Tall Buildings with a Diagrid Structure: A Machine Learning-Based Approach

We study the relationship between the architectural form of tall buildings and their structural response to a conventional seismic load. A series of models are generated by varying the top and bottom plan geometries of the buildings, and a steel diagrid structure is mapped onto their skin. A supervised machine learning approach is then adopted to learn the features of the aforementioned relationship. Six different classifiers, namely k-nearest neighbour, support vector machine, decision tree, ensemble method, discriminant analysis, and naive Bayes, are adopted to this aim, targeting the structural response as the building drift, i.e., the lateral displacement at its top under the considered external excitation. By focusing on the classification of the structural response, it is shown that some classifiers, like, e.g., decision tree, k-nearest neighbour and the ensemble method, can learn well the structural behavior, and can therefore help design teams to select more efficient structural solutions