Time History Data Research Articles

Study on the Prediction of Motion Response of Offshore Platforms Based on ResCNN-LSTM

In the random sea environment, offshore platforms are influenced by factors such as wind, waves, and currents, as well as their interactions, leading to complex motion phenomena that affect the safety of offshore platform operations. Consequently, accurately predicting the motion response of offshore platforms has long been a key focus in the fields of naval architecture and ocean engineering. This paper utilizes STAR-CCM+ to simulate time-history data of offshore platform motion responses under both regular and irregular waves. Furthermore, a predictive model combining residual convolutional neural networks and long short-term memory neural networks using neural network technology is also studied. This model utilizes an autoregressive approach to predict the motion responses of offshore platforms, with its predictive accuracy validated through comprehensive evaluations. Under regular wave conditions, the coefficient of determination (R2) for the platform’s heave and pitch responses consistently exceeds 0.99. Meanwhile, under irregular wave conditions, the R2 values remain generally above 0.4. Additionally, the model exhibits commendable performance in terms of Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. The aim of this study is to present a novel approach to predicting offshore platform motion responses, while providing a more scientific basis for decision-making in offshore platform operations.

An Experimental Study on the Effects of Nonuniform Vane Spacing on Forced Response Reduction in a Mistuned Rotor Blisk in a Multistage Axial Compressor

Abstract Stators with nonuniform vane spacing (NUVS) are known to effectively reduce the forced response of adjacent rotors by spreading the primary engine order (EO) excitation at a certain speed to a series of weaker excitations at nearby EOs over a wider speed range. To be used in a real gas turbine engine, it is essential to know the forced response vibratory reduction that can be achieved for a specific NUVS configuration at different operating conditions. The classical estimation method to predict the blade response reduction is to quantify the reduction of the forcing function at a potential resonance crossing by conducting a circumferential Fourier analysis of the asymmetric flow field. However, besides excitation reduction, the blade forced response also depends on other factors, such as damping and blade-to-blade interactions due to mistuning. To study the blade forced response reduction in a realistic environment, a comprehensive experimental study was conducted in the Purdue three-stage axial research compressor at three different loading conditions. The vibratory response of the first torsion (1 T) mode forced response of rotor 2 was measured by strain gages (SGs) for two different upstream stator 1 configurations: the symmetric 38-vaned stator 1 configuration and the NUVS stator 1 configuration with 18–20 vane halves. As expected, the strong 38EO-1T response from the symmetric stator reduced to a series of weaker responses from 35EO to 41EO in the NUVS configuration. The overlap of adjacent EO responses caused considerable beating phenomena in the blade SG time–history data. There was substantial blade-to-blade variation in blade response for both symmetric and asymmetric stator 1 configurations due to the inherent nonintentional mistuning in the rotor 2 blisk. This, in turn, causes a large blade-to-blade variation in the reduction factors. While higher responding blades tend to have larger reduction factors, some blades show almost no forced response reduction when switching to the NUVS stator. In addition, the reduction factors for each blade and the maximum reduction factor for the whole blisk also change significantly with loading conditions. The measured reduction factors and the peak response EO are not well predicted by the classical estimation method. This indicates that both damping and mistuning effects need to be considered in the NUVS reduction factor prediction.

Time history analysis of multi- storey building with basement story accounting for soil–structure interaction

Soil-Structure Interaction (SSI) is the effects of the soil (underlying and surrounding the structure) on the response of the structure. This study is a contribution in the soil structure interaction (SSI) field, as this interaction affects the seismic behavior of structures. Two series of Time History data were used in the analysis of reinforced concrete moment resisting frame building consisting of 9 stories with embedded basement storey building. The two approaches of SSI modeling that are substructure approach and direct approaches were compared with each other. Generally, and within the limits of this study, it was found that the responses of Substructure approach were identical with that of the direct approach in the two cases for all stories except for the Ground storey. It seems that SSI affects the higher stories rather than the lower stoerys. The dynamic analysis results comparison between the two approaches of modeling soil structure interaction, favored the substructure approach on the direct approach as the direct approach is time consuming. Also, the study found out that modeling the structure with only vertical spring led to doubtable results, which reflect present that is choosing this method represents the worst option. On the other hand, and according to the analysis results, study suggested that keeping use the fixed approach in design buildings (with embedded portion) does not represent the better choice. It is concluded that, the responses of Substructure approach is identical with that of the Direct approach in all cases for all stories except for the Ground storey. It seems that SSI affects the higher stories rather than the lower storey.

CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data

This study presents a convolutional neural network (CNN) deep learning approach for identifying damage in submerged structure-foundation systems using vibration data. Firstly, foundation damage in a lab-scale caisson-foundation system is simulated to measure time-history responses. Singular value decomposition (SVD) responses are derived from the time-history responses. Secondly, the 1-D CNN deep learning model is trained using both the time-history responses and SVD responses. Finally, the trained CNN models are implemented to evaluate the foundation damage under conditions of noise contamination and partially untrained data. The experimental results demonstrate the effectiveness of CNN models for damage identification and highlight the comparative strengths of time-history and SVD data. The CNN model trained using SVD data outperforms the other model when under noise contamination conditions, while the CNN model trained using time-history data maintains better accuracy in partially untrained data conditions. Integrating both types of data enhances the accuracy of damage classification.

Novel approach for precise identification of vibration frequencies and damping ratios from free vibration decay time histories data of nonlinear single degree of freedom models

The significance of Single Degree of Freedom (SDOF) systems lies in their ability to serve as foundational elements for modeling more complex dynamic problems. By capturing essential dynamic behavior with simplicity, SDOF models enable efficient analysis and comprehension of complex systems, justifying the investigation of these simplified models. In nonlinear scenarios, SDOF models result in time series data wherein vibration frequencies vary over time. Classically, time–frequency or Hilbert transforms applied to temporal responses are frequently used to identify the evolution of frequencies and damping ratio over time. These techniques provide results that reflect the spectrum composition achieved for the analyzed time window and present difficulties in precisely determining the magnitude and the exact instant of an effective frequency or damping ratio variation. In this sense, this work introduces a new methodology capable of accurately identifying the vibration frequency as a function of time, i.e., the instantaneous frequency, along with the instantaneous damping ratio. At this initial stage, the focus is on validating the methodology by comparing its performance with the classical approach based on time–frequency transforms. The initial results obtained from synthetic free vibration decay responses of SDOF nonlinear models highlight the accuracy of our findings compared to those obtained from time–frequency transforms. The presented methodology holds promise for further advancement, with potential impacts including structural damage identification, modal identification and nonlinear dynamic analysis.

A novel CNN architecture for robust structural damage identification via strain measurements and its validation via full-scale experiments

In this study, an innovative two-dimensional Convolutional Neural Network (2D CNN) architecture is proposed and investigated for the classification of bridge damage. Employing unique strain time-history data transformed into grayscale images, the approach seamlessly combines feature extraction and classification, allowing for the precise identification and categorization of structural damage. The method’s effectiveness was validated through field experiments on a full-scale bridge mock-up sample subjected to several controlled damage states under nonstationary, commercial vehicle loads. A wide range of realistic damage conditions, from minor to severe structural damage states, was included in the experimental scenarios together with inherent operational uncertainties. The robustness of the 2D CNN model was rigorously tested against fluctuating loads and introduced noise. Demonstrating remarkable accuracy, the 2D CNN successfully classified different damage states with over 95% accuracy, effectively identifying a damage state that was visually undetectable. Furthermore, the architecture proved to be highly versatile, effectively handling variations in the number of sensors. uncertainties included in the experimental data, and elevated levels of measurement noise.

Research on Structural Damage Identification of Shear Buildings Based on Modal Curvature Change

In the past few decades, various techniques for structural damage identification based on dynamic properties have been widely studied, among which modal curvature is the research hotspot, due to its high sensitivity to local damage. To realize story-level structural damage identification of shear buildings, this paper first derives the patterns of variation in modal curvature of the damaged structure. Then, a new damage index sensitive to local damage called the Modal Curvature Change modified with Softmax (MCCSM for short), is proposed to improve the accuracy of structural damage identification. Afterward, the validity of the damage index is verified by a shaking table test of a scaled RC frame structure model and its corresponding numerical model. The research results show that the damage location indicated by MCCSM corresponds well with the damage phenomenon in the shaking table test and numerical simulation; compared with using the theoretical mode shape, the MCCSM index can give comparable damage identification results using the mode shape obtained through operational modal analysis from vibration time-history data, which proves the feasibility in engineering practices; through numerical simulation, it is proved that the MCCSM index is applicable in a wide range of damage cases with different story position, number of damaged story, and damage extent.

A method for the simultaneous determination of the soil dry density and moisture content based on thermal effects

Abstract The thermodynamic parameters of soil are affected by both dry density and moisture content, leading to uncertainty in measuring moisture content using the heat source method. This study proposes a combined approach using a back propagation (BP) neural network and the point heat resource method to simultaneously determine soil dry density and moisture content. The segmented mean value extracted from the temperature time-history data during the cooling process of the heat source serves as the feature input, while measured values of dry density and moisture content serve as outputs. A calibrated BP neural network model is trained and utilized for simultaneous determination of both parameters. Numerical simulations and modeling tests demonstrate good agreement between inverse identification results and measurements, with root mean square errors of 1.65% for moisture content and 34.09 kg∙m-3 for dry density, along with coefficients of determination at 0.9482 and 0.9359 respectively. It is proved that the method combining soil thermal effect and BP neural network to measure soil dry density and moisture content is feasible.

A stress-controlled soil column test equipment for investigating water seepage-consolidation characteristics of the compacted soil

For deep-filled building site, it is not acceptable to neglect water seepage and land settlement caused by the coupling effect of wetting and loading. However, previous soil column tests employed in investigating the water seepage always failed to consider the vertical stress. In this article, a stress-controlled soil column test was conducted to investigate the effect of vertical stress on water seepage and deformation of the compacted soil. The soil column was equipped with a vertical loading device and a water-recharge device to control the boundary conditions and water sensors, tensiometers, dial indicators and a data collector to monitor the displacement and water movement. The soil column test was utilised to perform seepage tests on compacted loess with vertical stresses of 0 and 400 kPa. The time-history data of wetting front depth, volumetric water content (VWC), suction and vertical displacement have been monitored directly. The VWC and suction profiles, soil–water characteristic curves and hydraulic conductivity curves were then obtained with the monitored data. The test results demonstrated that the vertical stress affected the water seepage and consolidation of the compacted soil, but this effect gradually decreased as the depth of the soil column increased. Moreover, the reliability of the proposed test method was verified by the comparison of some available test results. The findings of this study contribute to a better understanding of water seepage and consolidation characteristics of the compacted soil subjected to vertical stress.

Quasi-static testing based structural modal parameter estimation

ABSTRACT Structural modal identification is generally implemented within a dynamic framework, requiring multidisciplinary knowledge and adequate operational experience. This preliminary study attempts to explore an easy-to-handle quasi-static alternative method for dynamics-based modal identification of beam-type structures. Acceleration time-history data are replaced by quasi-static deflections induced by slow moving loads. A concept of quasi-static deflection influence surface is proposed based on the theory of influence lines and the principle of virtual work. Its analytical expression is simplified into a deflection matrix by choosing specific points on the surface according to deflection measurement coordinates. The matrix form is divided by external loads to obtain a generalised quasi-static flexibility matrix, which is used to replace the inversion of the global stiffness matrix in the modal eigenvalue equation. The lumped-mass method is also employed to establish the mass matrix. Subsequently, the eigenvalue equation is analytically solved seeking for modal frequencies and mode shapeswithout performing modal tests. The feasibility of the proposed method has been successfully verified against three experimental examples including a continuous box girder with variable cross sections. It was observed that the modal frequencies and mode shapes estimated by the proposed method were very close to those given by the dynamically modal tests.

Robust Trajectory Prediction Using Random Forest Methodology Application to UAS-S4 Ehécatl

Accurate aircraft trajectory prediction is fundamental for enhancing air traffic control systems, ensuring a safe and efficient aviation transportation environment. This research presents a detailed study on the efficacy of the Random Forest (RF) methodology for predicting aircraft trajectories. The study compares the RF approach with two established data-driven models, specifically Long Short-Term Memory (LSTM) and Logistic Regression (LR). The investigation utilizes a significant dataset comprising aircraft trajectory time history data, obtained from a UAS-S4 simulator. Experimental results indicate that within a short-term prediction horizon, the RF methodology surpasses both LSTM and LR in trajectory prediction accuracy and also its robustness to overfitting. The research further fine-tunes the performance of the RF methodology by optimizing various hyperparameters, including the number of estimators, features, depth, split, and leaf. Consequently, these results underscore the viability of the RF methodology as a proven alternative to LSTM and LR models for short-term aircraft trajectory prediction.

Analisis Aselerasi Riwayat Waktu pada Wilayah Spesifik di Yogyakarta dengan Sumber Gempa Subduksi Berdasarkan Peta Gempa 2002

Indonesia is a country that is very prone to earthquake disasters. Earthquakes cause damage especially to buildings. In building planning, the usual earthquake load is the earthquake load from the spectral response. The use of earthquake loads from time history is very rarely used because of the difficulty of finding accurate data for an area. In this study, Alana Hotel and Apartment were chosen as research objects to find artificial time history. All earthquake data affecting Yogyakarta region were collected from several sources. Seismic hazard analysis in this study uses the PSHA (Probabilistic Seismic Hazard Analysis) method by calculating the main earthquake only. The selection of attenuation functions is based on the similarity of geological and tectonic conditions in Indonesia. These attenuation functions produce a seismic hazard curve and a uniform hazard spectrum response. The selection of time history data for bedrock is based on the results of the deaggregation process in the form of dominant magnitude and dominant distance from the subduction earthquake source. It is necessary to adjust the spectrum response in the time history data to fit the research location. Furthermore, earthquake waves from the bedrock are propagated to the surface. The results show that the peak time history acceleration value after surface propagation increased by 1.51 times than the time history in bedrock.

Convolutional neural network–based structural health monitoring framework for wind turbine blade

Wind energy is a renewable energy source with a significant impact on the date for harnessing it. Different types of wind turbines (WTs) are being built to generate affordable, dependable, and eco-friendly renewable wind energy. Over time, cracks are found to be developed in the blades of the WTs, which is considered the most common type of damage that ultimately causes the catastrophic failure of the structures. This study aims to develop a structural health monitoring (SHM) framework for localizing cracks of a WT blade using a convolutional neural network (CNN)–based deep learning algorithm. With that objective, the NACA (National Advisory Committee for Aeronautics) 63-412 profile (WT blade) of length 29 m was modelled in finite element analysis (FEA) ANSYS 2022 R2. A crack is introduced to the model by making a groove at different blade locations. The mode shape and natural frequencies are obtained and validated with those reported in the literature. Further, the blade is excited by an impact load applied at the tip of the blade, and acceleration time histories are collected. The acceleration time history data are converted to scalogram images and fed into the CNN algorithm for damage classification and localization. In this study, the concept of class activation maps is also utilized for visual representations of the input images’ areas that significantly influence a class’s classification score. The classified results show that using CNN can successfully localize the region of the cracks and motivate us to examine it on a laboratory-based model.

An Experimental and Detailed Kinetics Modeling Study of Norbornadiene in Hydrogen and Methane Mixtures: Ignition Delay Time and Spectroscopic CO Measurements

High-energy-density compounds such as norbornadiene (NBD) are being considered as potential cost-effective fuel additives, or partial replacements, for high-speed propulsion applications. To assess the ability of NBD to influence basic fuel reactivity enhancement and to build a database for developing future NBD kinetics models, ignition delay times were measured in two shock-tube facilities at Texas A&M University for H2/O2, CH4/O2, H2/NBD/O2, and CH4/NBD/O2 mixtures (ϕ = 1) that were highly diluted in argon. The reflected-shock temperatures ranged from 1014 to 2227 K, and the reflected-shock pressures remained near 1 atm for all of the experiments, apart from the hydrogen mixtures, which were also tested near 7 atm, targeting the second-explosion limit. The molar concentrations of NBD were supplemented to the baseline mixtures representing 1–2% of the fuel by volume. A chemiluminescence diagnostic was used to track the time history of excited hydroxyl radical (OH*) emission, which was used to define the ignition delay time at the sidewall location. Spectroscopic CO data were also obtained using a tunable quantum cascade laser to complement both the ignition and the chemiluminescence data. The CH4/O2 mixtures containing NBD demonstrated reduced ignition delay times, with a pronounced effect at lower temperatures. Conversely, this additive increased the ignition delay time dramatically in the H2/O2 mixture, which was attributed to changes in the fundamental chemistry with the introduction of molecules containing carbon bonds, which require stronger activation energies for ignition. Correlations were developed to predict the ignition delay time, which depends on species concentration, temperature, and pressure. Additionally, one tentative mechanism was tested, combining base chemistry from NUIGMech 1.1 with pyrolysis and oxidation reactions for NBD using the recent efforts from experimental and theoretical literature studies. The numerical predictions show that the rapid decomposition of NBD provides a pool of active H-radicals, significantly increasing the reactivity of methane. This study represents the first set of gas-phase ignition and CO time-history data measured in a shock tube for hydrogen and methane mixtures containing the additive NBD.

Oceanographic wave buoy motion as a 3D-vector field: Spectra, linear components and bound harmonics

This study analyses time history data measured by a widely used type of oceanographic buoy. The motion of the buoy is recorded as a three-component displacement vector with one vertical and two horizontal components. Focusing on the average shapes of the large events in the time series, we investigate the linear and nonlinear structure of the displacements and their inherent relationships. All three displacements show clear evidence of both linear and second-order contributions. Two types of nonlinear contributions are identified: the difference term representing slow oscillation on the mooring system, and the sum term oscillating at roughly twice the frequency of the linear waves. These terms are small vertically but larger horizontally. We examine the coupled structure of the vertical and horizontal motions with a conditioning analysis. The averaged time histories associated with large waves are found to obey a simple reciprocal relationship, and the average shape of the motion in space associated with large waves in the sea-state resembles that in a localised steep wave-group. The power spectrum of the vertical motion can be transformed into those in the horizontal plane. This work demonstrates that, as well as providing accurate spectral information of the sea state, oceanographic wave buoys can provide internally consistent time series for the vector displacement field with little observable distortion from the mooring system.

Conversion of Earthquake Vibrations in to Electricity

Energy is produced by nature, and humans have always sought to manipulate this source for their own gain. Different strategies for reaching this goal have been developed over time. Due to the underutilized yet significant energy that may be acquired from earthquakes,this article investigates a different source of power. By catching this energy and turning it into electricity, a novel sort of generation could be accomplished. It's not widely understood that seismic mechanical waves can be used as a source of energy, and there isn't much literature on the subject either. The focus of this research paper is to convert earthquake vibrations into electrical signals by using vibration sensor in Servo electric shake table apparatus. The model is tested for El Centro earthquake time history data, Chile earthquake data and for random sine waveforms.

Study on seismic response of unconnected piled raft with rubber mixed soil

Piled raft foundations are conveniently adopted as the feasible solution for foundations of high-rise buildings constructed across the globe. The analysis of seismic response of unconnected piled raft foundations, where the rigid connection between the raft bottom and pile top was separated by a layer of sand or aggregates, by incorporating the complex interactions between the foundation and soil is a challenge. Therefore, the focus of the current study is on the response analysis of these foundations by considering the soil-structure interactions under dynamic loading conditions. The numerical modelling is done in Opensees with the aid of STKO software, where the complex interactions between structural elements and soil can be modelled accurately. Several models were developed for a comparative analysis of conventional and unconnected piled raft foundations. Initially, a connected square piled raft with four bored piles of 9 m length was numerically modelled in a soil domain along with absorbing boundary conditions on the lateral sides. The effect of separation layer thickness on the seismic response of the foundation was studied by modelling various models of unconnected piled raft foundations. The thickness of the separation layer varied from 0.25 m, 0.5 m, 0.75 m and 1 m. The vertical settlement under static loading conditions was initially evaluated and the behaviour of the foundation under seismic excitation was analysed subsequently. The acceleration time history data of the El-Centro earthquake (1979) was given as an input motion at the base of the soil domain. The results of the numerical evaluation include the lateral displacements and accelerations that were measured at the top of the raft foundation. It was found that the variation in the thickness of the separation layer considerably affects the seismic response of the piled raft foundation.

MLPER: A Machine Learning-Based Prediction Model for Building Earthquake Response Using Ambient Vibration Measurements

Deep neural networks (DNNs) have gained prominence in addressing regression problems, offering versatile architectural designs that cater to various applications. In the field of earthquake engineering, seismic response prediction is a critical area of study. Simplified models such as single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems have traditionally provided valuable insights into structural behavior, known for their computational efficiency facilitating faster simulations. However, these models have notable limitations in capturing the nuanced nonlinear behavior of structures and the spatial variability of ground motions. This study focuses on leveraging ambient vibration (AV) measurements of buildings, combined with earthquake (EQ) time-history data, to create a predictive model using a neural network (NN) in image format. The primary objective is to predict a specific building’s earthquake response accurately. The training dataset consists of 1197 MDOF 2D shear models, generating a total of 32,319 training samples. To evaluate the performance of the proposed model, termed MLPER (machine learning-based prediction of building structures’ earthquake response), several metrics are employed. These include the mean absolute percentage error (MAPE) and the mean deviation angle (MDA) for comparisons in the time domain. Additionally, we assess magnitude-squared coherence values and phase differences (Δφ) for comparisons in the frequency domain. This study underscores the potential of the MLPER as a reliable tool for predicting building earthquake responses, addressing the limitations of simplified models. By integrating AV measurements and EQ time-history data into a neural network framework, the MLPER offers a promising avenue for enhancing our understanding of structural behavior during seismic events, ultimately contributing to improved earthquake resilience in building design and engineering.

Comparative study on a single-story RCC frame with large and small-scale magnetorheological damper subjected to seismic excitation

The magnetorheological fluid damper (MRD) is a commonly employed semi-active damping mechanism. The utilisation of this technology has the potential to mitigate the impact of natural hazards on structures, owing to its crucial ability to dissipate energy and its minimal energy consumption. This study aims to compare the performance of a reduced-scale reinforced concrete (RCC) frame with a large MR damper (MRD) attached diagonally, to that of an RCC frame with a smaller-scale MRD attached at different locations. The experimental evaluation of two frames was conducted utilising El-Centro time history data, which was obtained from MTS. The efficacy of the small-scale MRD was also assessed through the El Centro seismic loading before integrating to the RCC frame. Subsequently, it was integrated with the frame and exhibited effective performance in mitigating seismic excitation. The maximum force generated was measured at 2.03kN, accompanied by a notable reduction in displacement of 13.24%. The frame equipped with a diagonal damper directly installed at the beam-column joint exhibits load-carrying capacities that are 12.32% and 19.77% lower than those of the frame equipped with a single and double damper, respectively, positioned at a distance of L/3 from the joint. In comparison to a diagonally placed large damper, the implementation of a small-scale magnetorheological damper (MRD) resulted in a reduction of displacements by 7.99% and 17.59% for single and double MRD configurations installed on the frame, respectively. However, this implementation also led to an increase in forces and alterations in crack patterns. The experimental findings indicate that the inclusion of a substantial quantity of smaller MRD can be a viable approach to mitigating structural responses in regions prone to seismic activity, while still maintaining the aesthetic integrity of the structure.

Flow time history representation and reconstruction based on machine learning

Based on deep learning technology, a new spatiotemporal flow data representation and reconstruction scheme is proposed by using flow time history (FTH) data instead of flow snapshots. First, the high-dimensional nonlinear flow system is reduced to a low-dimensional representation latent code using the FTH autoencoder model. Second, the mapping from physical space to latent code space is built using mathematical and machine-learning schemes. Finally, FTH at unavailable positions in physical space is generated by the FTH generator. The proposed scheme is validated by three case studies: (i) representing and recovering the FTH data of periodic laminar flow around a circular cylinder at Re = 200 and generating high-resolution laminar flow data; (ii) reconstructing complex FTH of flow past cylinder at Re = 3900 which including laminar and turbulent flow region and generating three-dimensional high-resolution turbulent flow data, respectively; (iii) representing and generating multi-variable turbulent flow data simultaneously using the multi-channel model. The results show that the proposed scheme is an effective low-dimensional representation for complex flow time variant features, which is suitable for both laminar and turbulent FTH data to generate spatiotemporal high-resolution FTH data in three-dimensional space.