Probability Distribution of Engineering Properties of Soil at Telecomunication Sites in Indonesia

Dianty, Marelianda Al; Yahaya, Ahmad Shukri; Ahmad, Fauziah

doi:10.12983/ijsrk-2014-p0143-0150

Cited by 4 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Uno de ellos consiste en un análisis geoespacial, donde se ubican las muestras de suelo recolectadas con sus coordenadas para poder agrupar sus resultados de los ensayos de laboratorio y, de esta forma, generar una clasificación de los datos por zonas en el territorio nacional. El otro método aplica un análisis geoestadístico, que en él se aplica un modelo geoespacial, pero también se predice la distribución de los resultados del ensayo de laboratorio en las zonas contempladas en el país [4][5] [6][7]. Esta base de datos estadísticos se realizó en tres agrupaciones por provincias:…”

Section: Metodologíaunclassified

“…La situación ideal para obtener estos datos es poder realizar una cantidad estadísticamente significativa de ensayos geotécnicos de campo y laboratorio para efectuar un análisis probabilístico de los resultados que permita cuantificar adecuadamente la incertidumbre asociada a los materiales presentes en el proyecto. Sin embargo, en algunos proyectos, como por ejemplo viviendas, no se cuenta con el presupuesto para poder realizar una investigación geotécnica que permita la identificación completa y precisa de las condiciones de heterogeneidad del sitio, por lo que la representatividad de los muestreos realizados no siempre alcanza los niveles de confianza deseables [2] [4].…”

Section: Introductionunclassified

See 1 more Smart Citation

Zonificación geotécnica mediante estadísticos descriptivos para suelos de Costa Rica

Baltodano-Goulding

2023

View full text Add to dashboard Cite

Con el objetivo de proveer información adicional para la realización de análisis y diseños geotécnico mediante un enfoque probabilístico cuando no se cuenta con una cantidad de datos que permita un procesamiento estadístico de los parámetros requeridos del suelo, se elaboró una zonificación geotécnica mediante estadísticos descriptivos para los suelos encontrados en Costa Rica, tomando en consideración el mapa geotécnico y las condiciones climáticas de Costa Rica. Para el ámbito geotécnico, se consideraron los mapas de zonificación geotécnica establecidos en el Código de Cimentaciones, mientras que en el meteorológico se tomaron en cuenta el Atlas Climatológico del Instituto Meteorológico Nacional. Como parte de los resultados se obtuvieron los valores promedio y la desviación estándar de diferentes tipos de suelos presentes en la zonificación efectuada en el país. La información geotécnica de los diferentes materiales se obtuvo de campañas geológicas y estudios geotécnicos privados. Estos datos son importantes porque al considerarlos, se logra disminuir la incertidumbre asociada a las características del suelo.

show abstract

Section: Metodologíaunclassified

Section: Introductionunclassified

Zonificación geotécnica mediante estadísticos descriptivos para suelos de Costa Rica

Baltodano-Goulding

2023

View full text Add to dashboard Cite

show abstract

“…The lowest value of SSE and the higher the value of R 2 indicated the quality of the relationship between proposed models. Performance indicators like mean square error (MSE), the index of agreement (IA) and prediction of accuracy (PA) were used to predict soil plasticity properties [7].…”

Section: Study Areamentioning

confidence: 99%

Prediction of Soil Compression Index in Teziutlán, Mexico

Dianty¹,

Wu²

2020

AMM

View full text Add to dashboard Cite

One of the important soil materials for the evaluation of settlement due to consolidation of soil clay is the compression index (Cc). The assessment of its value through laboratory testing is usually time consuming and expensive. Hence to find the most appropriate model prediction, the paper is examined the compression index comparison with soil plasticity material (water content, liquid limit, plasticity index). The analysis is used by multiple regression analysis and some performance indicators such as Coefficient of Determination (R2), Index of Agreement (IA), Prediction Accuracy (PA), Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE). The result identified the proposed model by Skempton’s, Sidharan, Nagaraj’s and Hough’s are suitable for the compression index (Cc) equation model. Another essential point, the research is found error measurement for soil plasticity index properties. Thus, this prediction model is useful to develop a preliminary assessment of the settlement of structures and carry out suitable action for landslides in Teziutlan, Mexico.

show abstract

“…In addition, the scale parameter effects the scaling of the PDF curve. In the geotechnical engineering reliability, the two-parameter Weibull distribution is one of the most commonly used models [18,19]. The PDF of the two-parameter Weibull distribution can be written as Eq.…”

Section: Weibull Distributionmentioning

confidence: 99%

“…Through the comparison and selection of the classical distributions (normal distribution, log-normal distribution, Weibull distribution, gamma distribution, etc. ), some previous studies have shown that many geotechnical parameters will accept a Weibull distribution as the optimal PDFs [17][18][19][20]. However, there are some unsolved issues in the application process of the Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

Inference of the optimal probability distribution model for geotechnical parameters by using Weibull and NID distributions

Gong¹,

Wang²,

Wang³

2019

J. vibroeng.

View full text Add to dashboard Cite

To obtain the optimal probability distribution models of geotechnical parameters, the goodness of fit of the normal information diffusion (NID) distribution and Weibull distribution were investigated and compared for actual engineering samples and Monte Carlo (MC) simulated samples. Two datasets from actual engineering parameters (the strength of a rock mass and the average wind speed) were used to test the fitting abilities of these two distributions. The results show that the parameters of the NID distribution are easily estimated, the Kolmogorov-Smirnov (K-S) test results of the NID distribution are smaller than those of the Weibull distribution, and the NID distribution curves can perfectly reflect the stochastic volatility of geotechnical parameters with small sample sizes. The sample size effects on the fitting accuracy of the NID distribution and Weibull distribution were also investigated in this paper. Eight simulated samples with different sample sizes, namely, 15, 20, 30, 50, 100, 200, 500, and 1000, were produced by using the MC method based on two known Weibull distributions. The results show that with an increase in the sample size, the K-S test results of the NID distribution gradually decrease and tend to converge, while the chi-square test results of the NID distribution remain low and are always lower than those of the Weibull distribution. The cumulative probability values of the NID distribution are larger than those of the Weibull distribution and are always equal to 1.0000. Finally, the comparison of the fitting accuracy between the NID distribution and normalized Weibull distribution was also analyzed.

show abstract

Probability Distribution of Engineering Properties of Soil at Telecomunication Sites in Indonesia

Cited by 4 publications

References 5 publications

Zonificación geotécnica mediante estadísticos descriptivos para suelos de Costa Rica

Zonificación geotécnica mediante estadísticos descriptivos para suelos de Costa Rica

Prediction of Soil Compression Index in Teziutlán, Mexico

Inference of the optimal probability distribution model for geotechnical parameters by using Weibull and NID distributions

Contact Info

Product

Resources

About