Abstract

The structural monitoring, which is becoming increasingly common, makes it necessary to choose one or more methods of processing and analyzing the obtained data. This choice is key to meeting the objectives of this monitoring. Considering that there are many methods with different capacities and requirements, this choice is a complex problem. This paper presents a methodology for the assessment and selection of methods for the identification of dynamic response properties of buildings using vibration monitoring. In this work, well-known methods based on time domain analysis (subspace-state) and non-parametric methods based on the frequency domain are studied when applied to seismic vibration records for a reinforced concrete building instrumented with a network of tri-axial force-balance accelerometers. For two and a half years, the sensor network has recorded close to 500 sensitive earthquakes. Nonetheless, it only includes those with a magnitude higher than 4.8 (M_L or M_W) and that occurred between September, 2015 and September, 2016. Using six versions of identification methods, modal response parameters are determined for each of the two main directions of the building, considering different levels of information acquired by the sensor network (floor information). An assessment of the performance of each method is conducted based on the number of identified modal frequencies and damping ratios; their variability (coefficient of variation); and the processing time of each method. As a result and conclusion regarding the use of this methodology, it is not only possible to choose the most convenient method for processing vibration records applied to structural health monitoring (SHM) according to its own objective, but also to progressively improve the design of the sensor network.

Abstract

The impact force of debris flow is not only an important indicator of the risk assessment of debris flow and the strength impact resistance of buildings against debris flow, but also an important parameter in the design of various debris flow prevention projects (such as the check dam and the drainage channel, etc.). The pressure sensors are arranged at different positions (monitoring points) on the upstream face of the check dam. By changing the slope of the drainage channel, the bulk density of debris flow and the slope gradient of the upstream face of the check dam, the time history curves of the impact force at the monitoring points under different experiment conditions are obtained. The characteristic value of impact force of debris flow acting on the surface of sand retaining dam is analyzed, and the evolution law of mean value and maximum value of impact force of debris flow at the same detection location with the above conditions is obtained. The mean value and maximum value of debris flow impact force at dif-ferent detection locations under the same working condition are analyzed to obtain the evolu-tion law of debris flow impact force at different locations, and then the distribution trend of debris flow impact force on the upstream face of the check dam is obtained. The research results provide scientific and reasonable theoretical basis and technical support for the stability analysis of the check dam, so as to better serve the disaster prevention and reduction of debris flow, which will improve the technical level of the debris flow prevention project to a certain extent.

Abstract

This is the review of the Structural Engineering Update course carried out at the Regional Center for Technological Developments for Seismology and Seismic Engineering Construction (CeDeReTec, Mendoza, Argentina) at the end of the year 2019. The presentations are shown and also reports on the activities carried out within the framework of the Project “Destructive Potential Generated by the Earthquake of Ecuador of April 16, 2016” are reported.

Abstract

Abstract

The internal solitary waves (ISWs) in the ocean carry huge energy and pose a serious threat to the safety of underwater vehicle. In order to obtain the dynamic response of the submarine under the action of ISWs, the amplified SUBOFF model was placed in a large numerical water tank with 5000×200×500 m dimension in which the water depth was 500 m. The Korteweg-de Vries (KdV) theory was adopted to simulate the generation of ISWs in the two-layer flow, and overset grid technology was used to ensure the grid quality during the submarine movement. The results show that before the ISWs peak reaches the position of the submarine, the submarine will move, which causes the submarine to sink, move laterally and pitch. The longitudinal velocity is obviously greater than the lateral velocity, and the submarine finally hits the water tank bottom. With the increase of the ISWs amplitude, the time needed for submarine to reach the bottom increases, and the pitching angle increases greatly. The ISWs amplitude has no effect on the motion trajectory of the submarine's center of gravity, and has little effect on the lateral and longitudinal velocity. With the decrease of submergence depth of the submarine, the time required for submarine to reach the bottom will also increase, and the motion trajectory will change, but the trend of change is basically the same. The submergence depth has little influence on the variation range of lateral velocity, longitudinal velocity and pitching angle. '''Key words:''' internal solitary waves, submarine, dynamic response, numerical simulation

Abstract

Real-time monitoring of oil-gas wellbore temperature profiles provides important support for drilling strategy adjustment and model parameter calibration. MEMS-based micro-measurers can collect fullwellbore temperature data by flowing with drilling fluid circulation, but converting their time-series records to well depth relies on idealized steady-motion assumptions that neglect wall collisions, sticking, and local flow disturbances, introducing systematic positioning errors. In this study, a deep learning-based time-depth conversion method is proposed. A gated recurrent unit (GRU)-based temporal neural network is developed to extract motion-state features from six-axis dynamic signals, and a bounded velocity correction mechanism is introduced to compensate deviations from the idealized terminal velocity. The results show that: (1) The proposed model effectively learns motion-state deviations from dynamic response data and provides stable velocity correction under complex downhole conditions. (2) The corrected time-velocity curve exhibits transient fluctuations relative to the idealized terminal velocity, accurately capturing non-ideal behaviors while preserving directional stability. (3) During model training, the loss function decreases progressively and stabilizes, indicating good convergence and training stability. (4) Comparative analysis shows that the proposed GRU-Full method achieves a mean absolute anchor-point error of 1.23 m (0.24% of the well depth), outperforming the MLP and LSTM alternatives. Ablation experiments confirm that both the physical constraints and regularization terms contribute substantially to the model accuracy. This study enhances the spatial mapping accuracy of temperature data acquired by micro-measurers under complex downhole dynamic conditions. The established physically constrained deep learning framework provides a new technical pathway for refined wellbore thermal-field characterization and intelligent drilling decision-making.

Abstract

As oil and gas exploration progressively extends into deeper and structurally complex reservoirs, dynamic buckling of down-hole tubular strings under impact loading has emerged as a critical issue, posing significant risks to drilling and completion integrity. This paper presents a fully coupled three-dimensional finite element framework that integrates a Cowper–Symonds rate-dependent plasticity model and an Arbitrary Lagrangian–Eulerian (ALE) formulation to capture both strain-rate sensitivity and fluid–structure interaction—two key aspects that have been largely overlooked in prior tubular string buckling analyses. The model incorporates tubular, casing, and fluid domains under representative down-hole conditions and is validated through systematic mesh convergence and energy balance checks. Simulation results indicate that impact loading produces intense stress concentration and abrupt displacement at the tubular string bottom. Axial deformation alternates periodically between compression and tension, reaching a maximum axial displacement of 0.437 cm and exhibiting pronounced hysteretic energy dissipation. Spatially, the response is non-uniform: severe velocity oscillations (peak 9.07 m/s) occur at the bottom, whereas packer-induced constraints attenuate the top velocity by 75.6%. Axial acceleration dominates the response (peak 460.44 m/s2), far exceeding radial components. The numerical analysis further identifies two critical vulnerability zones: the maximum equivalent stress (519.52 MPa) occurs at the top due to wavereflection superposition, while the initial impact creates a local stress hotspot of 434.35 MPa at the bottom—both locations representing key sites for buckling initiation and subsequent fatigue damage. By quantifying the spatiotemporal evolution of deformation, motion, and stress fields, this study reveals how axial dynamics interact with boundary constraints to trigger dynamic buckling. The findings provide a theoretical basis for the optimum design and safe deployment of deep-well tubular strings, while demonstrating the predictive capability of advanced finite element modeling for complex downhole impact scenarios where experimental data are limited.