scholarly journals Development of a Nondestructive Impulse Device and Damage Model for Unreinforced Concrete

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Shane D. Boone ◽  
Paul J. Barr ◽  
James A. Bay ◽  
Marvin W. Halling
Keyword(s):  
Damage Model ◽  
Damage Index ◽  
Concrete Compressive Strength ◽  
Cumulative Energy ◽  
Compression Waves ◽  
Proposed Model ◽  
Impulse Excitation ◽  
Cycles To Failure ◽  
Short Impulse

Unconstrained compression waves were measured using a newly developed, nondestructive, short impulse excitation device developed for long-term structural health monitoring. The measurements, using this innovative device, were used to determine the variation in the first longitudinal modal frequency as a function of loading magnitude and loading cycles to failure of various concrete mixes. Longitudinal frequency and cumulative energy variations were found to be a function of concrete compressive strength. These results imply that higher-strength concrete more easily absorbs energy and restricts the growth of microcracks. Based on the results, a new damage model is proposed that was shown to correlate with measured values to within 7%. This proposed model was found to have a closer correlation than Miner’s hypothesis and damage index models from other reviewed research.

A Proposed Damage Model for RC Bridge Columns under Cyclic Loading

2001 ◽  
Vol 17 (2) ◽  
pp. 261-290 ◽  
Author(s):  
Riyadh A. Hindi ◽  
Robert G. Sexsmith
Keyword(s):  
Cyclic Loading ◽  
Low Cycle Fatigue ◽  
Damage Model ◽  
Damage Index ◽  
Bridge Columns ◽  
Hysteretic Behavior ◽  
Concrete Column ◽  
Cycle Fatigue ◽  
Proposed Model ◽  
Ultimate Displacement

This paper defines a damage index based on the predicted hysteretic behavior of a concrete column. The model yields a damage index at a point in the time history for the element, based on the predicted monotonic response from the point in time to failure. The model takes into account the parameters that describe the hysteretic behavior: stiffness degradation, strength deterioration, and ultimate displacement reduction. Therefore, the damage model is accumulative and it combines energy, ductility, and low-cycle fatigue. The model is based on the work needed to fail a reinforced concrete column monotonically after it experiences a cyclic loading. The model modifies the ultimate displacement that the column can achieve, due to low-cycle fatigue in the longitudinal reinforcement using the Coffin-Manson rule in combination with Miner's hypothesis. The proposed model is applied to bridge columns tested by others, and compared to existing damage indices. The proposed model gives a realistic prediction of damage throughout the loading cycles for several test specimens investigated.

Fatigue Life of the Human Teeth: A Continuum Damage Approach

2019 ◽  
Vol 43 ◽  
pp. 1-19
Author(s):  
Theddeus Tochukwu Akano
Keyword(s):  
Fatigue Life ◽  
Damage Mechanics ◽  
Stress Amplitude ◽  
Continuum Damage Mechanics ◽  
Continuum Damage ◽  
Elastoplastic Model ◽  
Human Teeth ◽  
Proposed Model ◽  
Number Of Cycles ◽  
Cycles To Failure

Normal oral food ingestion processes such as mastication would not have been possible without the teeth. The human teeth are subjected to many cyclic loadings per day. This, in turn, exerts forces on the teeth just like an engineering material undergoing the same cyclic loading. Over a period, there will be the creation of microcracks on the teeth that might not be visible ab initio. The constant formation of these microcracks weakens the teeth structure and foundation that result in its fracture. Therefore, the need to predict the fatigue life for human teeth is essential. In this paper, a continuum damage mechanics (CDM) based model is employed to evaluate the fatigue life of the human teeth. The material characteristic of the teeth is captured within the framework of the elastoplastic model. By applying the damage evolution equivalence, a mathematical formula is developed that describes the fatigue life in terms of the stress amplitude. Existing experimental data served as a guide as to the completeness of the proposed model. Results as a function of age and tubule orientation are presented. The outcomes produced by the current study have substantial agreement with the experimental results when plotted on the same axes. There is a notable difference in the number of cycles to failure as the tubule orientation increases. It is also revealed that the developed model could forecast for any tubule orientation and be adopted for both young and old teeth.

Modeling of unforced demand response programs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Jalili ◽  
Pierluigi Siano
Keyword(s):  
Demand Response ◽  
Load Shifting ◽  
Response Models ◽  
Temperature Changes ◽  
Proposed Model ◽  
Equipment Failures ◽  
Demand Response Programs ◽  
Simultaneous Implementation ◽  
Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

scholarly journals Prediction of concrete compressive strength due to long term sulfate attack using neural network

2014 ◽  
Vol 53 (3) ◽  
pp. 627-642 ◽  
Author(s):  
Ahmed M. Diab ◽  
Hafez E. Elyamany ◽  
Abd Elmoaty M. Abd Elmoaty ◽  
Ali H. Shalan
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Sulfate Attack ◽  
Concrete Compressive Strength

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

Analysis of thermo-mechanical coupling damage behavior in long-term performance of microelectromechanical systems actuators

2021 ◽  
pp. 105678952110339
Author(s):  
Jiaxing Cheng ◽  
Zhaoxia Li
Keyword(s):  
Damage Mechanics ◽  
Microelectromechanical Systems ◽  
Mechanical Performance ◽  
Damage Model ◽  
Irreversible Damage ◽  
Damage Behavior ◽  
Thermal Actuators ◽  
Long Term Performance ◽  
Term Performance

Effective numerical analysis is significant for the optimal design and reliability evaluation of MEMS, but the complexity of multi-physical field couplings and irreversible damage accumulation in long-term performance make the analysis difficult. In the present paper, the continuum damage mechanics method is used to develop a creep damage model and conduct long-term performance analysis for MEMS thermal actuators with coupled thermo-mechanical damage behavior. The developed damage model can make a connection between the material deterioration due to microstructure changes and the macroscopic responses (the change of thermo-mechanical performance or structure failure). The numerical simulations of coupled thermo-mechanical behavior in long-term performance are implemented using the finite element method, which is validated through comparison with previous literature. The numerical results demonstrate that the proposed damage model and numerical method can provide effective assessment in the long-term performance of MEMS thermal actuators.

A Global Socio-economic-medico-legal Model for the Sustainability of Longitudinal Electronic Health Records

2006 ◽  
Vol 45 (03) ◽  
pp. 240-245 ◽  
Author(s):  
A. Shabo
Keyword(s):  
Electronic Health Records ◽  
Healthcare Providers ◽  
Financial Assets ◽  
Healthcare Organizations ◽  
Health Records ◽  
Record Keeping ◽  
Proposed Model ◽  
Legal Model ◽  
Electronic Health

Summary Objectives: This paper pursues the challenge of sustaining lifetime electronic health records (EHRs) based on a comprehensive socio-economic-medico-legal model. The notion of a lifetime EHR extends the emerging concept of a longitudinal and cross-institutional EHR and is invaluable information for increasing patient safety and quality of care. Methods: The challenge is how to compile and sustain a coherent EHR across the lifetime of an individual. Several existing and hypothetical models are described, analyzed and compared in an attempt to suggest a preferred approach. Results: The vision is that lifetime EHRs should be sustained by new players in the healthcare arena, who will function as independent health record banks (IHRBs). Multiple competing IHRBs would be established and regulated following preemptive legislation. They should be neither owned by healthcare providers nor by health insurer/payers or government agencies. The new legislation should also stipulate that the records located in these banks be considered the medico-legal copies of an individual’s records, and that healthcare providers no longer serve as the legal record keepers. Conclusions: The proposed model is not centered on any of the current players in the field; instead, it is focussed on the objective service of sustaining individual EHRs, much like financial banks maintain and manage financial assets. This revolutionary structure provides two main benefits: 1) Healthcare organizations will be able to cut the costs of long-term record keeping, and 2) healthcare providers will be able to provide better care based on the availability of a lifelong EHR of their new patients.

scholarly journals Improved numerical model for fatigue cumulative damage of mechanical structure considering load sequence and interaction

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199530
Author(s):  
Bixiong Huang ◽  
Shuci Wang ◽  
Shuanglong Geng ◽  
Xintian Liu
Keyword(s):  
Fatigue Life ◽  
Theoretical Basis ◽  
Life Prediction ◽  
Damage Model ◽  
Cumulative Damage ◽  
Load Spectrum ◽  
Random Load ◽  
Mechanical Parts ◽  
Proposed Model ◽  
Load Sequence

To more accurately predict the fatigue life of components under the action of random loads, it is necessary to explore the influence of the interaction between the load sequence and the load on the life prediction. Based on the Manson-Halford method and Corten-Dolan model, this paper establishes a fatigue cumulative damage model that takes into account both the load order and the interaction between loads, and also takes into account the loads near the fatigue limit. The fatigue life of mechanical parts under random load can be calculated through this model, which provides a theoretical basis for life prediction under random load spectrum. The fatigue life of mechanical parts under random load can be calculated through this model, which provides a theoretical basis for life prediction under random load spectrum. Comparing the calculation results of the proposed model with the results of Palmgren Miner, Manson-Halford method, and Corten-Dolan model, it is found that the fatigue damage model established can reasonably predict the fatigue life of parts. Comparison and verification of examples further prove the accuracy and reliability of the proposed model.

scholarly journals Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1671
Author(s):  
Jibing Gong ◽  
Cheng Wang ◽  
Zhiyong Zhao ◽  
Xinghao Zhang
Keyword(s):  
Auxiliary Information ◽  
Automatic Generation ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Online Study ◽  
Path Graph ◽  
Proposed Model ◽  
Meta Path

In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods.

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