Distinct element modelling of the seismic response of historical masonry constructions: insight on the out-of-plane collapse of façades

Façades belonging to historical masonry constructions typically fail by out-of-plane mechanisms. The estimate of their out-of-plane capacity is not a trivial task, due to the different possible collapse modes (overturning, bending, disaggregation, leaf separation, sliding) and to the discontinuous nature of masonry, influencing the non-linear seismic behaviour of walls. Simplified approaches, proposed by building codes, mainly based on the mechanics of the rigid block, may not always be suitable for the purpose. Indeed, they disregard the real morphology of masonry, which instead influences weaker failure mechanisms (such as disaggregation and leaf separation). Furthermore, they neglect the interaction of the façade with the rest of the building and its interlocking with transversal walls. These shortcomings can be overcome resorting to distinct element method (DEM), in which masonry is modelled as an aggregation of discrete units and no-thickness interfaces and the actual morphology of constructions is considered. In this paper, DEM is adopted to investigate the out-of-plane seismic behaviour of façades through non-linear analyses, by focusing on vertical bending and overturning failure mechanisms. The former is studied by comparing results of shake table tests on both single-leaf and double-leaf masonry walls to dynamic simulations in which real accelerograms are applied. The latter is analysed by performing non-linear static analyses on the Romanesque church of St. Maria Maggiore in Tuscania, Italy, by focusing on its façade. Distinct element method provided a realistic description of the behaviour of façades under earthquake loadings, in terms of both seismic capacity, crack pattern and failure mode

Condition-based maintenance in hydroelectric plants: A systematic literature review

Industrial maintenance has become an essential strategic factor for profit and productivity in industrial systems. In the modern industrial context, condition-based maintenance guides the interventions and repairs according to the machine’s health status, calculated from monitoring variables and using statistical and computational techniques. Although several literature reviews address condition-based maintenance, no study discusses the application of these techniques in the hydroelectric sector, a fundamental source of renewable energy. We conducted a systematic literature review of articles published in the area of condition-based maintenance in the last 10 years. This was followed by quantitative and thematic analyses of the most relevant categories that compose the phases of condition-based maintenance. We identified a research trend in the application of machine learning techniques, both in the diagnosis and the prognosis of the generating unit’s assets, being vibration the most frequently discussed monitoring variable. Finally, there is a vast field to be explored regarding the application of statistical models to estimate the useful life, and hybrid models based on physical models and specialists’ knowledge, of turbine-generators

Measurement of sigma(e+ e- -> pi+ pi-) from threshold to 0.85 GeV^2 using Initial State Radiation with the KLOE detector

We have measured the cross section of the radiative process e+e- -> pi+pi-gamma with the KLOE detector at the Frascati phi-factory DAPHNE, from events taken at a CM energy W=1 GeV. Initial state radiation allows us to obtain the cross section for e+e- -> pi+pi-, the pion form factor |F_pi|^2 and the dipion contribution to the muon magnetic moment anomaly, Delta a_mu^{pipi} = (478.5+-2.0_{stat}+-5.0_{syst}+-4.5_{th}) x 10^{-10} in the range 0.1 < M_{pipi}^2 < 0.85 GeV^2, where the theoretical error includes a SU(3) ChPT estimate of the uncertainty on photon radiation from the final pions. The discrepancy between the Standard Model evaluation of a_mu and the value measured by the Muon g-2 collaboration at BNL is confirmed.Comment: 17 pages, 11 figures, revised treatment of FSR uncertainty, version to appear on Physics Letters

Measurement of σ(e+e−→π+π−γ)\sigma(e^+e^-\to \pi^+ \pi^- \gamma) and extraction of σ(e+e−→π+π−)\sigma(e^+e^-\to \pi^+\pi^-) below 1 {\rm GeV} with the KLOE detector

We have measured the cross section $\sigma(e^+e^-\to \pi^+\pi^- \gamma)$ at an energy $W=m_\phi=1.02$ GeV with the KLOE detector at the electron-positron collider DA$\Phi$NE. From the dependence of the cross section on the invariant mass of the two-pion system, we extract $\sigma(e^+e^-\to \pi^+\pi^-)$ for the mass range $0.35<s<0.95$ GeV$^2$. From this result, we calculate the pion form factor and the hadronic contribution to the muon anomaly, $a_\mu$.Comment: 20 pages, 12 figure

A Data-Driven Framework for Small Hydroelectric Plant Prognosis Using Tsfresh and Machine Learning Survival Models

Maintenance in small hydroelectric plants (SHPs) is essential for securing the expansion of clean energy sources and supplying the energy estimated to be required for the coming years. Identifying failures in SHPs before they happen is crucial for allowing better management of asset maintenance, lowering operating costs, and enabling the expansion of renewable energy sources. Most fault prognosis models proposed thus far for hydroelectric generating units are based on signal decomposition and regression models. In the specific case of SHPs, there is a high occurrence of data being censored, since the operation is not consistently steady and can be repeatedly interrupted due to transmission problems or scarcity of water resources. To overcome this, we propose a two-step, data-driven framework for SHP prognosis based on time series feature engineering and survival modeling. We compared two different strategies for feature engineering: one using higher-order statistics and the other using the Tsfresh algorithm. We adjusted three machine learning survival models—CoxNet, survival random forests, and gradient boosting survival analysis—for estimating the concordance index of these approaches. The best model presented a significant concordance index of 77.44%. We further investigated and discussed the importance of the monitored sensors and the feature extraction aggregations. The kurtosis and variance were the most relevant aggregations in the higher-order statistics domain, while the fast Fourier transform and continuous wavelet transform were the most frequent transformations when using Tsfresh. The most important sensors were related to the temperature at several points, such as the bearing generator, oil hydraulic unit, and turbine radial bushing