The purpose of the work is to provide an experimental benchmark on the seismic behavior of tunnels, with the final aim of calibrating numerical and analytical design methods. A series of plane-strain centrifuge tests with dynamic loading on a model tunnel was, therefore, carried out at the Schofield Centre of the Cambridge University Engineering Department (CUED). Four samples of dry uniform fine sand were prepared at two different densities, in which an aluminum-alloy tube was installed at two different depths. The tube was instrumented with strain gauges to measure hoop forces and bending moments at significant locations. To monitor the amplification of ground motion from the base to the surface, vertical arrays of accelerometers were placed in the soil model and along the box. The instrumentation also included linear variable differential transformers (LVDTs) that measured the soil surface settlement during all test phases. The test procedure and the results are described in this paper, showing the evolution of both accelerations and internal forces along the tunnel lining during the model earthquakes.

Despite significant research advances on the seismic response analysis, there is still an urgent need for validation of numerical simulation methods for prediction of earthquake response and damage. In this respect, seismic monitoring networks and proper modelling can further support validation studies, allowing more realistic simulations of what earthquakes can produce. This paper discusses the seismic response of the “Pietro Capuzi” school in Visso, a village located in the Marche region (Italy) that was severely damaged by the 2016–2017 Central Italy earthquake sequence. The school was a two-story masonry structure founded on simple enlargements of its load-bearing walls, partially embedded in the alluvial loose soils of the Nera river. The structure was monitored as a strategic building by the Italian Seismic Observatory of Structures (OSS), which provided acceleration records under both ambient noise and the three mainshocks of the seismic sequence. The evolution of the damage pattern following each one of the three mainshocks was provided by on-site survey integrated by OSS data. Data on the dynamic soil properties was available from the seismic microzonation study of the Visso village and proved useful in the development of a reliable geotechnical model of the subsoil. The equivalent frame (EF) approach was adopted to simulate the nonlinear response of the school building through both fixed-base and compliant-base models, to assess the likely influence of soil–structure interaction on the building performance. The ambient noise records allowed for an accurate calibration of the soil–structure model. The seismic response of the masonry building to the whole sequence of the three mainshocks was then simulated by nonlinear time history analyses by using the horizontal accelerations recorded at the underground floor as input motions. Numerical results are validated against the evidence on structural response in terms of both incremental damage and global shear force–displacement relationships. The comparisons are satisfactory, corroborating the reliability of the compliant-base approach as applied to the EF model and its computational efficiency to simulate the soil–foundation–structure interaction in the case of masonry buildings.

We describe an optimal randomized MapReduce algorithm for the problem of triangle enumeration that requires O(E^3/2/(M√m) rounds, where m denotes the expected memory size of a reducer and M the total available space. This generalizes the well-known vertex partitioning approach proposed in (Suri and Vassilvitskii, 2011) to multiple rounds, significantly increasing the size of the graphs that can be handled on a given system. We also give new theoretical (high probability) bounds on the work needed in each reducer, addressing the "curse of the last reducer". Indeed, our work is the first to give guarantees on the maximum load of each reducer for an arbitrary input graph. Our experimental evaluation shows the scalability of our approach, that it is competitive with existing methods improving the performance by a factor up to 2X, and that it can significantly increase the size of datasets that can be processed.