scholarly journals Machine Learning-Based Modeling for the Duration of Load Effect in Wood Structural Members Under Long-Term Sustained Load

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 17903-17915
Author(s):  
Peng Qi ◽  
Minjuan He ◽  
Mengwei Li ◽  
Xiuzhi Zheng ◽  
Zheng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sustained Load ◽  
Structural Members ◽  
Load Effect ◽  
Duration Of Load

scholarly journals Prediction of Damage Accumulation Effect of Wood Structural Members under Long-Term Service: A Machine Learning Approach

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1243
Author(s):  
Zheng Li ◽  
Duo Tao ◽  
Mengwei Li ◽  
Zhan Shu ◽  
Songshi Jing ◽  
...  
Keyword(s):  
Machine Learning ◽  
Damage Accumulation ◽  
Goodness Of Fit ◽  
Machine Learning Algorithms ◽  
Cycle Performance ◽  
Model Parameters ◽  
Timber Structures ◽  
Structural Members ◽  
Accumulation Effect

It is well known that wood structural members can stand a relatively heavy load in the short term but will gradually get weaker if the load is applied for a longer period. This phenomenon is caused by the damage accumulation effect in wood and should be appropriately considered during the design of timber structures. Although various formulation methods (also known as classical models) have been proposed to evaluate the damage accumulation effect in wood, the calibration of model parameters is very time-consuming. Our work proposes a novel method to deal with the damage accumulation effect in wood that involves the application of machine learning algorithms. The proposed algorithm considers a multi-objective optimization process with a combination of goodness-of-fit and complexity. Long-term experimental data of typical wood species are used for developing the machine learning based damage accumulation model. Compared with existing pre-formulated models, our model managed to reduce the complexity of the model structure and give sufficiently accurate and unbiased predictions. This study aims to provide a novel tool for evaluating the damage accumulation in wood structural members, and the proposed model can further support the life-cycle performance assessment of timber structures under long-term service scenarios.

Duration of load effects in lumber. Part I: A fracture mechanics approach

1982 ◽  
Vol 9 (3) ◽  
pp. 502-514 ◽  
Author(s):  
Kenneth Johns ◽  
Borg Madsen
Keyword(s):  
Fracture Mechanics ◽  
Stress Ratio ◽  
Failure Time ◽  
Creep Function ◽  
Load Effect ◽  
Mechanics Model ◽  
Current Design ◽  
Load Effects ◽  
Duration Of Load

In Part I of this paper, the deterioration of the strength of lumber with continued application of constant stress, called the duration-of-load effect, is treated using a viscoelastic, limited ductility fracture mechanics model. The model is explained and developed in a general way, then modified for use with commercial lumber. The problems of assigning correct creep function parameters and values of stress ratio for use in calculations involving the model are discussed. The evident weakening of boards surviving a long-term test can be used to project a failure time that is longer than the test period. Numerical results are shown and compared with the Madison curve, the basis for current design codes. Parts II and III of this paper demonstrate experimental verification and discuss design implications.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Comparison of reliability evaluation methods between China and Canada standards for wood structures featuring duration of load

2020 ◽  
Vol 66 (1) ◽  
Author(s):  
Qiongyao Wu ◽  
Shuang Niu ◽  
Enchun Zhu
Keyword(s):  
Reliability Analysis ◽  
Analysis Method ◽  
Safety Level ◽  
Wood Structures ◽  
Load Effect ◽  
Key Factor ◽  
Live Loads ◽  
High Level ◽  
Partial Factor ◽  
Duration Of Load

Abstract Duration of load (DOL) is a key factor in design of wood structures, which makes the reliability analysis of wood structures more complicated. The importance of DOL is widely recognized, yet the methods and models through which it is incorporated into design codes vary substantially by country/region. Few investigations of the effect of different model assumptions of DOL and other random variables on the results of reliability analysis of wood structures can be found. In this paper, comparisons are made on the reliability analysis methods that underlie the China and the Canada standards for design of wood structures. Main characteristics of these two methods, especially the way how DOL is treated are investigated. Reliability analysis was carried out with the two methods employing the same set of material properties and load parameters. The resulted relationships between reliability index β and resistance partial factor γR* (the β–γR* curves) for four load combinations are compared to study the safety level indicated by the two methods. The comparison shows that the damage accumulation model (Foschi–Yao model) in the Canada analysis method is highly dependent on the type and duration of load, resulting in more conservative design than the China analysis method in loading cases dominated by dead load, but less conservative design in cases of high level of live loads. The characteristics of the load effect term of the performance function are also found to make considerable difference in reliability levels between the two methods. This study aims to provide references for researchers and standard developers in the field of wood structures.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Psychiatric comorbid disorders of cognition: a machine learning approach using 1175 UK Biobank participants

2020 ◽  
Vol 23 (4) ◽  
pp. 140-145
Author(s):  
Chenlu Li ◽  
Delia A Gheorghe ◽  
John E Gallacher ◽  
Sarah Bauermeister
Keyword(s):  
Machine Learning ◽  
Cognitive Decline ◽  
Psychiatric Disorders ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Imaging Study ◽  
Cognitive Change ◽  
Poor Mental Health ◽  
Uk Biobank

BackgroundConceptualising comorbidity is complex and the term is used variously. Here, it is the coexistence of two or more diagnoses which might be defined as ‘chronic’ and, although they may be pathologically related, they may also act independently. Of interest here is the comorbidity of common psychiatric disorders and impaired cognition.ObjectivesTo examine whether anxiety and/or depression are/is important longitudinal predictors of cognitive change.MethodsUK Biobank participants used at three time points (n=502 664): baseline, first follow-up (n=20 257) and first imaging study (n=40 199). Participants with no missing data were 1175 participants aged 40–70 years, 41% women. Machine learning was applied and the main outcome measure of reaction time intraindividual variability (cognition) was used.FindingsUsing the area under the receiver operating characteristic curve, the anxiety model achieves the best performance with an area under the curve (AUC) of 0.68, followed by the depression model with an AUC of 0.63. The cardiovascular and diabetes model, and the covariates model have weaker performance in predicting cognition, with an AUC of 0.60 and 0.56, respectively.ConclusionsOutcomes suggest that psychiatric disorders are more important comorbidities of long-term cognitive change than diabetes and cardiovascular disease, and demographic factors. Findings suggest that psychiatric disorders (anxiety and depression) may have a deleterious effect on long-term cognition and should be considered as an important comorbid disorder of cognitive decline.Clinical implicationsImportant predictive effects of poor mental health on longitudinal cognitive decline should be considered in secondary and also primary care.

scholarly journals International agricultural trade forecasting using machine learning

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Munisamy Gopinath ◽  
Feras A. Batarseh ◽  
Jayson Beckman ◽  
Ajay Kulkarni ◽  
Sei Jeong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Agricultural Trade ◽  
Supervised Machine Learning ◽  
Trade Patterns ◽  
Subjective Assessments ◽  
History Of ◽  
Network Approaches ◽  
Traditional Approaches

Abstract Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database

2021 ◽  
Author(s):  
Nikos Fazakis ◽  
Elias Dritsas ◽  
Otilia Kocsis ◽  
Nikos Fakotakis ◽  
Konstantinos Moustakas
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Machine Learning Techniques ◽  
Learning Techniques
Sign in / Sign up

Export Citation Format

Share Document