The Experts below are selected from a list of 3471 Experts worldwide ranked by ideXlab platform
Zhang Qian - One of the best experts on this subject based on the ideXlab platform.
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Research on accuracy of visualized Settlement Prediction based on optimized GM(1,1)
Journal of Engineering Design, 2009Co-Authors: Zhang QianAbstract:The final Settlement deformation of soft soil foundation has great impact on design safety and normal usage of constructing project such as highway,railway,airport and port terminal.In light of the Settlement Prediction result of grey model(GM),the selected value of GM directly determines the correctness of Prediction result.Therefore how to select value and the rule to select value is the core of accuracy of GM.Based on optimized GM(1,1),grey-visualization software for Settlement Prediction was developed by VB.Through analysis on accuracy of different time intervals as well as compare between Prediction result and actual inspected data,it is demonstrated that this method can predict Settlement of soft soil foundation more accurately and has practical engineering value.
Wang Yu-hai - One of the best experts on this subject based on the ideXlab platform.
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Soft Soil Foundation Settlement Prediction Based on Hyperbolic Model
Jiangsu Construction, 2012Co-Authors: Wang Yu-haiAbstract:In construction of a soft soil foundation,the vacuum and preloading method was used to consolidate the soft soil in test section.Take the measured vertical deformation data as the basis for the establishment of the hyperbolic Prediction model and the final Settlement forecast.By comparison with the measured data shows that,the projected value based on the hyperbolic model are close to the measured values,consistent high between subsidence Prediction curves and the observed curve,can be a better response to changes of the soil Settlement in the whole process,and have got the result that the hyperbolic model was feasible in the reinforcement of soft soil foundation Settlement Prediction by vacuum and preloading method..
Holger R. Maier - One of the best experts on this subject based on the ideXlab platform.
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Neural network based stochastic design charts for Settlement Prediction
Canadian Geotechnical Journal, 2005Co-Authors: Mohamed A. Shahin, Mark B. Jaksa, Holger R. MaierAbstract:Traditional methods of Settlement Prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the Settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the Settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted Settlement. Artificial neural networks, like more traditional methods of Settlement Prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of Settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of Settlement....
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Settlement Prediction of shallow foundations on granular soils using b spline neurofuzzy models
Computers and Geotechnics, 2003Co-Authors: Mohamed A. Shahin, Holger R. Maier, Mark B. JaksaAbstract:Abstract The design of shallow foundations on granular soils is generally controlled by Settlement rather than bearing capacity. As a consequence, Settlement Prediction is a major concern and is an essential criterion in the design process of shallow foundations. At present, consistent accurate Prediction of Settlement of shallow foundations on granular soils has yet to be achieved using many numerical modelling techniques. Recently, multi-layer perceptrons (MLPs) trained with the back-propagation algorithm have been applied successfully to Settlement Prediction of shallow foundations on granular soils. However, a shortcoming of MLPs is that the knowledge that is acquired during training is distributed across their connection weights in a complex manner that is often difficult to interpret. Consequently, the rules governing the relationships between the network input/output variables are difficult to quantify. One way to overcome this problem is to use neurofuzzy networks in which the acquired knowledge can be translated into a set of fuzzy rules that describe the relationships between the network inputs and the corresponding outputs in a transparent fashion. In the present paper, the ability of neurofuzzy networks to predict Settlement of shallow foundations on granular soils and to assist with providing a better understanding regarding the relationships between Settlement and the factors affecting Settlement is assessed. The sensitivity of neurofuzzy models to a number of stopping criteria is investigated and the models obtained are compared in terms of Prediction accuracy, model parsimony and model transparency. The impact of incorporating existing engineering knowledge on neurofuzzy model performance and interpretation is also investigated. The type of neurofuzzy networks used in this research are B-spline networks that are trained with the adaptive spline modelling of observation data (ASMOD) algorithm. The results indicate that B-spline neurofuzzy networks are capable of predicting well the Settlement of shallow foundations on granular soils and are able to provide a transparent understanding of the relationships between Settlement and the factors affecting it. It is found from this research that neurofuzzy models that use the Bayesian Information Criterion (BIC) are able to strike a balance between model accuracy, parsimony and transparency. The results also indicate that modifying neurofuzzy networks by incorporating existing engineering knowledge can improve model performance and enhance the interpretation of the constructed model.
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Artificial Neural Network based Settlement Prediction Formula for Shallow Foundations on Granular Soils
2002Co-Authors: Mohamed A. Shahin, Mark B. Jaksa, Holger R. MaierAbstract:The problem of estimating the Settlement of shallow foundations on granular soils is very complex and not yet entirely understood. The geotechnical literature has included many formulae that are based on several theoretical or experimental methods to obtain an accurate, or near-accurate, Prediction of such Settlement. However, these methods fail to achieve consistent success in relation to accurate Settlement Prediction. Recently, artificial neural networks (ANNs) have been used successfully for Settlement Prediction of shallow foundations on granular soils and have been found to outperform the most commonly-used traditional methods. This paper presents a new hand-calculation design formula for Settlement Prediction of shallow foundations on granular soils based on a more accurate Settlement Prediction from an artificial neural network model. The design formula presented is a quick tool from which Settlement can be calculated easily without the need for computers.
Hyun Il Park - One of the best experts on this subject based on the ideXlab platform.
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Field performance of a genetic algorithm in the Settlement Prediction of a thick soft clay deposit in the southern part of the Korean peninsula
Engineering Geology, 2015Co-Authors: Hyun Il ParkAbstract:Abstract Long-term Settlement data of a thick soft clay deposit improved by vertical drains frequently shows different field Settlement behaviors from laboratory-driven Predictions by conventional theories due to the variability and uncertainty of the soil properties, modeling simplifications, and types of Prediction methods. This paper presents the field application of a back-analysis method based on a genetic algorithm (GA) to evaluate the performance of a new Settlement Prediction method compared with conventional graphical Settlement Prediction methods, such as the hyperbolic method and the Asaoka method. The GA back-analysis method shows better flexibility in modifying surcharging plans and adaptability to multi-layered thick soft soil deposits at the early stages of post-construction Settlement. Thus, this new Settlement Prediction method enables geotechnical engineers to subsequently modify the heights of surcharge fills in accordance with field Settlement data monitored in the interim for rapid and cost-effective construction. The comparative results show that the GA back-analysis method is capable of superior field performance in Settlement Predictions compared with two conventional graphical methods, within a margin of less than 200 mm in a thick soft clay deposit with multiple layers under complex loading conditions.
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parameter evaluation and performance comparison of msw Settlement Prediction models in various landfill types
Journal of Environmental Engineering, 2007Co-Authors: Hyun Il Park, Borinara Park, Seung R Lee, Daejin HwangAbstract:Estimation of Settlement for municipal solid waste (MSW) landfills is critical to the successful site operation and maintenance as well as to the future development of the sites. In this paper, the six existing Settlement Prediction models are analyzed according to the three landfill types. 15 MSW landfill sites are classified into three categories, Types I, II, and III depending on their Settlement activity levels and fill age. Measured Settlement data of each landfill site are plotted as a strain curve on a logarithmic time scale, and the slope of the curve is used to determine a Settlement activity level. First, the parameters of each Prediction model are evaluated to determine their typical values for each type of the landfills. The results will serve as a basis for engineers to determine if their calculated model parameters are within the typical ranges. Second, each model’s performance of predicting Settlement for the landfill sites is analyzed against the measured Settlement data to compare the mod...
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evaluation of decomposition effect on long term Settlement Prediction for fresh municipal solid waste landfills
Journal of Geotechnical and Geoenvironmental Engineering, 2002Co-Authors: Hyun Il Park, Seungrae LeeAbstract:A considerable amount of Settlement occurs due to the decomposition of municipal solid waste (MSW) in landfills over a period of years. Therefore, the effect of biological decomposition governs the long-term Settlement characteristics of municipal solid waste landfills. In this study, we investigated the long-term Settlement characteristics by applying a number of Prediction methods to fresh MSW sites and predicting the Settlement curves. Most proposed methods, excluding the power creep law, successfully predicted long-term Settlement only if accelerated logarithmic compression due to decomposition of biodegradable MSW was included in the Settlement Prediction.
V. Suthagaran - One of the best experts on this subject based on the ideXlab platform.
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Long-term Settlement Prediction for wastewater biosolids in road embankments
Resources Conservation and Recycling, 2013Co-Authors: M. Disfani, Arul Arulrajah, V. SuthagaranAbstract:An innovative research study was undertaken to characterize the Settlement characteristics of aged wastewater biosolids to facilitate its long-term Settlement Prediction when used as fill material in road embankment applications. Settlement can be sub-divided into compression due to consolidation and deformation attributed to biodegradation. Results of an extensive geotechnical laboratory evaluation including compaction characteristics, shear strength parameters, coefficient of consolidation, compression index, swell index and coefficient of secondary consolidation were used to predict the consolidation Settlement of biosolids in road embankments. Other relevant parameters for biodegradation Settlement Prediction, such as organic content, pH and electrical conductivity of the biosolids were also determined. The biodegradation induced Settlement of a road embankment built with aged biosolids was subsequently analyzed by applying an analytical method used previously for municipal solid waste landfills. The adopted model shows that the rate of biodegradation Settlement reduces with the reduction in pH values of biosolids. The model also suggests that the time taken for full process of biodegradation decreases dramatically with pH value of the biosolids between 0 and 6 and then increases exponentially with pH value of the biosolids between 8 and 14. A framework has been developed to predict the total Settlement of wastewater biosolids in road embankments for end-users.