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Correlación entre el CBR de laboratorio, el índice DCP y propiedades físicas y mecánicas de suelos granulares

= Correlation between laboratory CBR, DCP index and physical and mechanical properties of granular soils

= ¹

Favio Paul Portilla Yandún

https://orcid.org/0000-0001-6178-1582

Facultad de Ingeni= ería Civil y Mecánica, Universidad Técnica de Ambato, Ambato, Ecuador.

fp.p= ortilla@uta.edu.ec =

	Artículo de Investigación Científica y Tecnológica Enviado: 09/08/2022 Revisado: 24/09/2022 Aceptado: 31/10/2022 Publicado:23/11/2022 DOI: https://doi.org/10.33262/concienciadigital.v5i4.1= .2396

Cít= ese:		Por= tilla Yandún, F. P. (2022). Correlación entre el CB= R de laboratorio, el índice DCP y propiedades físicas y mecánicas de suelos granulares. ConcienciaDigital, 5(4.1), 45-59.= https://doi.org/10.33262/concienciadigital.v5i4.1= .2396
		CONCIENCIA DIGITAL, = es una revista multidiscipli= nar, trimestral, que se publicará en soporte electrónico tiene como = misión contribuir a la formación de profesionales competentes con visión humanística y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante = su intervención cambios positivos en la sociedad. https://concienciadigital.org La revista es editada por la Editorial Ciencia Digital (Editorial de prestig= io registrada en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec
		Esta revista está protegida bajo una licencia Creative Commons Attribution Non= Commercial No Derivatives 4.0 International. Copia de la licencia: http://creati= vecommons.org/licenses/by-nc-nd/4.0/
Pal= abras claves: Suelos, subrasante, compactación, CBR, DCP, propiedad= es, correlación.		Resumen Introducción. El presente estudio propone la utilización de un ensayo simple de evaluac= ión del suelo, como es la Penetración Dinámica de Cono (DCP) y su correlación= con el CBR (Relación de Soporte California), un ensayo ampliamente conocido y utilizado para evaluación de suelos de subrasante, subbase y base, el cual es necesario para el diseńo= y control de la estructura de pavimentos. Este estudio no pretende reemplaz= ar la metodología de evaluación normada de suelos, sino fortalecer la base d= el diseńo preliminar de pavimentos. Objetivo. Determinar el nivel de correlación existente entre el CBR, DCP y propiedades físicas y mecánicas= de suelos granulares. Metodología. Para la realización del estudio se realizaron ensayos de campo y laboratorio para determinar las propiedades, para lo cual se extrajeron 30 muestras de suelo en la sierra norte y cent= ro del Ecuador. La obtención de correlaciones entre estas propiedades se rea= lizó utilizando el método estadístico de mínimos cuadrados. Resultados. Se obtuvo la distribución granulométrica, límites de consistencia, clasificación SUCS y AASHTO, densidad seca máxima y humedad óptima, DCP y CBR de las muestras ensayadas. Con estos valores se analizaron varias correlaciones de dos y = tres variables entre estas las propiedades. Conclusión. Se concluyó que existe un buen nivel de correlación entre varias de las propiedades del s= uelo analizadas. La correlación CBR-DCP no es significativamente aceptable, sin embargo, se pueden obtener valores cercanos de CBR mediante el uso de otr= as correlaciones presentadas en esta investigación. Área de estudio o rama de la ciencia: Ingeniería Civil
Keywords: Soils, subgrade, compaction, CBR, DCP, properties, correlation.		Abstrac= t Introduction. The present study proposes the use of a simple = soil evaluation test, such as the Dynamic Cone Penetration (DCP) and its correlation with the CBR (California Bearing Ratio), a widely known and u= sed test for the evaluation of subgrade, subbase, and base soils. of and subb= ase, which is necessary for the design and control of the pavement structure. = This study is not intended to replace the standardized soil evaluation methodology, but to strengthen the basis for preliminary pavement design.= Objective. Determine the level of correlation between CBR, DCP and physical and mechanical properties of granular soils. Methodology. To conduct t= he study, field and laboratory tests were performed to determine the propert= ies, for which thirty soil samples were extracted in the northern and central highlands of Ecuador. Correlations between these properties were analyzed using the least squares statistical method. Results. The granulometric distribution, consistency limits, SUC= S and AASHTO classification, maximum dry density, and optimum moisture, DCP and= CBR of the tested samples were obtained. With these values, several correlati= ons of two and three variables between these properties were analyzed. Con= clusion. It was concluded that there is a satisfactory level of correlation between several of the soil properties analyzed. The CBR-DCP correlation is not significantly acceptable, however, close CBR values can be obtained by using other correlations presented in this research.

Introducción<= /b>

La evaluación de la resistencia al esfuerzo cortante es necesaria en la mayorí= a de los problemas de estabilidad de suelos (Bowles, 1981). Los ensayos CBR y DCP se utilizan a menudo para determinar el nivel de dens= idad del suelo superficial en estructuras viales (Arbianto et al., 2021). El fac= tor DCP (Dynamic Cone Penetration) mide indirectamente esta resistencia= que varía de acuerdo con el tipo de suelo y es determinado mediante un ensayo in situ en condiciones naturales del suelo (American Society for Testing and Materials [ASTM], 2009). Por otro lad= o, el factor CBR (California Bearing Ratio) mi= de también dicha resistencia, aplicada a suelos de subrasante, subbase y base de pavimentos (Bermúdez et al., 2016), mediante el ensayo de muestras = en condiciones de densidad y humedad controladas en laboratorio. La prueba de relación de soporte California (CBR) es una prueba de resistencia simple que compara la capacidad de carga de un material con la de una piedra triturada bien graduada (Ntirenganya, 2020).

El ensayo CBR es costoso, relativamente lento de realizar, mientras que el DCP= es liviano y portátil (Al-Refeai & Al-Suhaibani, 1996), y es un ensayo de rápida, sencilla ejecución y bajo costo para la estimación de esta resisten= cia de los suelos (Paige-Green, 2011). Sin embargo, realizar este ensayo puede requerir mucho trabajo debido a la = masa de caída del martillo (Nguyen & Mohajerani, 2012).

Según lo reportado por Livneh (1989), la resistencia a la fricción del suelo en la realización del ensayo DCP puede variar además según el grado de inclinació= n de la punta cónica del equipo entre 30° y 60°, haciendo que los valores CBR calculados con las ecuaciones de correlación difieran en cierto porcentaje.=

Los valores obtenidos del DCP dependen de las condiciones de ensayo. Debido a q= ue varios procedimientos de prueba afectarían los resultados en al menos un 10= por ciento, es preferible realizar todas las pruebas de DCP a través de un orif= icio perforado (Chen et al., 2001).

Algunas correlaciones han sido reportadas entre el DCP y CBR (Al-Refeai & Al-Suhaibani, 1996), con la finalidad de determinar los valores calculados = CBR a partir de los datos obtenidos en campo del DCP, para diferentes tipos de suelo. En la norma de la American Society for Testing and Materials (ASTM, 2009) D6951/D6951M-09, se presen= ta la ecuación de correlación entre el DCP y el CBR in situ aplicable a cualquier tipo de suelo. En Ecuador existen varias investigaciones de correlación entre estos= dos parámetros. Una de estas es el estudio realizado por Ga= llegos (2022), donde se analiza la correlación entre el DCP y el CBR de laboratori= o de los suelos de varios cantones de la provincia de Tungurahua.

Feleke & Araya (2016), presentaron las correlaciones e= ntre el DCP y CBR de laboratorio en condiciones saturadas y no saturadas en suel= os granulares y suelos finos de subrasantes localizados en Mekelle, Etiopía, obteniendo mediante análisis de regresión, altos coeficientes R²= de correlación. Wilches et al. (2018), analizaron la ecuación de correlación entre estos factores, para una muestra de suelos finos de subrasante de la ciudad de Sincelejo, Colombia, obteniendo un alto coeficie= nte de correlación, indicando que se puede considerar como una alternativa confiable para la determinación indirecta del CBR basado en el índice DCP.<= o:p>

En su estudio Zumrawi (2014), concluye que el DCP es la prueba más simple y ec= onómica y se prefiere para predecir el CBR in situ para las diferentes capas de pavimento. Varias correlaciones fueron desarrolladas entre el DCP y CBR. La relación propuesta se desarrolló a partir de las propiedades índice del sue= lo.

Por otro lado, Encinares & Encela (2022) acotan en su investigación que el usuario debe tener cuidado al obtener datos de la ecuación de correlación desarrollada entre el DCP y CBR de laboratorio, particularmente para suelos fuera del área de estudio y que es necesario realizar trabajos adicionales aumentando el número de muestras para validar la correlación establecida.

Con estos antecedentes, el objetivo de esta investigación es presentar el grado= de correlación experimental entre el CBR de laboratorio y DCP para suelos granulares de subrasante de la región andina del centro y norte de Ecuador.=

Aplicaremos, un muestreo representativ= o de los suelos granulares en estado natural a los lados de la carretera Panamericana, cada 14 km., desde la ciudad de Tulcán hasta la ciudad de Riobamba, en Ecuador.

Metodología

Las muestras fueron obtenidas de suelos naturales de subrasante, clasificados s= egún SUCS (Sistema Unificado de Clasificación de Suelos) y AASHTO, como suelos granulares (no cohesivos). El número de muestras recolectadas fue de 30, me= diante un muestreo aleatorio simple, considerado estadísticamente como el límite e= ntre pequeńas y grandes muestras, y para el cual los parámetros estadísticos no sufren mayores variaciones.

El tramo estudiado fue el callejón interandino a lo largo de la carretera panamericana (E-35) cada 14 km. desde la ciudad de Tulcán hasta la ciudad de Riobamba. Las muestras se obtuvieron a uno de los costados de la carretera,= en secciones de corte, con el fin de tener suelo natural.

Ensayos de Campo y Laboratorio

Para la obtención de cada muestra se realizó una calicata rectangular de 1.0 m x= 1.0 m de área y 1.0 m de profundidad, retirando la capa de suelo vegetal. Se to= mó una muestra alterada de aproximadamente 30 kg. en cada punto, para realizar= los ensayos de laboratorio respectivos. Los ensayos de campo y laboratorio que = se realizaron fueron: Densidad de Campo (cono y arena), DCP, Análisis Granulométrico, Límite Líquido, Límite Plástico, Proctor Modificado y CBR de laboratorio, siguiendo las normas AASHTO y ASTM correspondientes para cada ensayo.

El análisis de las curvas CBR vs. Densidad Seca, se realizó para una penetraci= ón del pistón de 0.1 y 0.2 plg. y el valor CBR para cada muestra, se escogió c= omo el mayor de los dos, para el 95% de la densidad seca máxima.

Criterios estadísticos para la obtención de correlaciones

Para la obtención de las correlaciones, se partió del fundamento de que las prop= iedades índices y mecánicas analizadas de los suelos, tienen una relación matemátic= a con una o varias propiedades. De esta manera se aplicaron algunas leyes de regresión, expresadas en ecuaciones de dos y tres variables que permitieron estimar una de las propiedades a partir del conocimiento de la o las otras.=

De esta manera, los modelos que se adoptarán responden a ecuaciones de la form= a:

Donde A, B, C, ... y a, b, c, ... son parámetros estimados mediante el método de correlación lineal de Mínimos Cuadrados.

Resultados

Granulometría, Plasticidad y Clasificación = de suelos

La clasificación SUCS Y AASHTO de las 30 muestras de suelo estudiadas se puede observar en la tabla 1, con los respectivos valores de granulometría de las fracciones gruesa y fina de los suelos, y los valores de los límites de consistencia e índices plásticos respectivos.

Los porcentajes de grava (% retiene tamiz No. 4) están entre 0 y 24%, arena (% = pasa tamiz No. 4) entre 29 y 79%, y finos (% pasa tamiz No. 200) entre 17 y 71%.= Los valores de límite líquido varían entre 23 y 70%, mientras que los valores de índice plástico varían entre 0 y 9%.

Tabla SEQ Tabla \* ARABIC 1

Clasificación de los suelos estudiados según los sistemas SUCS y AASHTO

Muestra No.<= o:p>

Referencia

Retiene #4 <= o:p>

%=

Pasa #200

%=

Límite Líqui= do %

Límite Plást= ico %

Índice Plást= ico %

SUCS

AASHTO<= /o:p>

1

Huaca

1

49

51.09

45.14

5.95

SM

A-5(4)

2=

San Gabriel<= o:p>

0=

60

43.23

34.88

8.35

ML

A-5(5)<= /o:p>

3

Bolívar

0

48

37.41

33.54

3.87

SM

A-4(3)

4=

El Ángel

0=

71

40.38

33.71

6.67

ML

A-4(7)<= /o:p>

5

Mira

5

60

31.71

24.61

7.10

ML

A-4(5)

6=

Chota

10

26

29.43

27.34

2.09

SM

A-2-4

7

Ibarra

4

47

32.45

25.79

6.66

SM

A-4(2)

8=

Atuntaqui

1=

56

33.31

29.90

3.41

ML

A-4(4)<= /o:p>

9

Otavalo

0

61

34.23

29.20

5.03

ML

A-4(5)

10

Cajas

6=

54

37.47

35.54

1.93

ML

A-4(3)<= /o:p>

11

Tabacundo

4

56

33.78

30.37

3.41

ML

A-4(4)

12

Cochasquí

1=

20

25.74

24.53

1.21

SM

A-2-4

13

Guayllabamba

4

26

28.90

30.23

-1.33

SM

A-2-4

14

Oyacoto=

10

35

23.26

23.16

0.10

SM

A-2-4

15

Pomasqui

1

27

25.00

23.12

1.88

SM

A-2-4

16

Cumbayá=

2=

38

30.15

26.16

3.99

SM

A-4(1)<= /o:p>

17

Cutuglahua

2

64

36.47

29.10

7.37

ML

A-4(6)

18

Alóag

1=

49

69.89

60.91

8.98

SM

A-5(5)<= /o:p>

19

Machachi

2

30

29.52

23.14

6.38

SM

A-2-4

20

Chasqui=

20

17

36.90

38.34

-1.44

SM

A-2-4

21

Lasso

3

25

32.00

27.65

4.35

SM

A-2-4

22

Latacunga

0=

57

35.73

36.83

-1.10

ML

A-4(4)<= /o:p>

23

Salcedo

2

29

26.04

26.78

-0.74

SM

A-2-4

24

Pansaleo

24

19

27.09

23.46

3.63

SM

A-2-4

25

Unamuncho

13

39

30.22

25.54

4.68

SM

A-4(1)

26

Ambato<= /o:p>

2=

48

28.92

22.68

6.24

SM

A-4(3)<= /o:p>

27

Huachi G.

3

51

27.72

24.44

3.28

ML

A-4(3)

28

Mocha

1=

47

43.76

41.25

2.51

SM

A-5(3)<= /o:p>

29

San Andrés

4

43

25.57

23.28

2.29

SM

A-4(2)

30

Riobamba

11

29

23.38

20.45

2.93

SM

A-2-4

Las propiedades mecánicas de las muestras de sue= lo analizadas, en los ensayos de Densidad de campo, Proctor Modificado, CBR y = DCP se presentan en la tabla 2. Particularmente, los valores CBR varían entre 7= .8 y 53.8 %, los valores DN varían entre 3.26 y 50.41 mm/golpe.

Tabla SEQ Tabla \* ARABIC 2

Propiedades Mecánicas de los suelos estudiados.

Muestra No.<= o:p>

D.S in-situ

kg/m³

D.S máxima

kg/m³

Humedad Ópti= ma %

CBR<= /o:p>

%=

DN

mm/golpe

1

1,143

1,390

28.3

15.0

50.41

2=

1,183

1,393

28.9

7.8

7.28

3

1,195

1,485

24.8

16.7

3.26

4=

1,149

1,412

29.7

14.3

14.81

5

1,378

1,685

16.6

21.0

8.11

6=

1,716

1,783

17.1

30.7

10.82

7

1,293

1,501

23.2

19.4

2.51

8=

1,613

1,733

15.9

33.8

22.13

9

1,228

1,555

20.9

20.7

13.38

10

1,317

1,558

21.8

10.8

13.28

11

1,425

1,596

20.5

15.0

4.08

12

1,547

1,716

13.9

44.0

5.73

13

1,288

1,726

12.7

38.0

10.06

14

1,473

1,756

12.5

45.8

13.88

15

1,592

1,796

12.7

53.0

19.58

16

1,089

1,708

16.7

32.7

19.69

17

1,186

1,626

17.0

34.5

21.64

18

0,838

1,156

38.7

4.8

31.77

19

1,181

1,529

19.5

31.5

14.53

20

0,883

1,076

27.9

43.2

11.15

21

1,261

1,554

20.6

19.0

16.29

22

0,988

1,562

19.6

52.0

29.52

23

1,854

1,649

14.2

50.0

12.87

24

2,129

1,855

10.8

50.0

7.40

25

1,441

1,687

17.4

53.8

11.10

26

1,513

1,696

17.0

26.7

12.60

27

1,319

1,718

15.3

48.4

11.21

28

1,251

1,528

23.5

28.0

12.45

29

1,679

1,746

13.0

27.0

5.48

30

1,714

1,820

13.3

45.0

5.43

Correlación 1: Límite Líquido – DCP - Densi= dad Seca In Situ

Se utilizó el método de correlación de mínimos cuadrados con un número de 30 muestras, obteniendo un coeficiente de regresión lineal de 0.66. La ecuació= n de la correlación es .

Figura <= b>1<= /p>
Correlación Límite Líquido – DCP – Densidad Seca in situ

Con esta correlación, se utilizaron los valores de límite líquido y DCP determi= nados mediante los ensayos de laboratorio y de campo respectivamente, para calcul= ar los valores de densidad seca in situ mediante la ecuación dada, obteniendo = los siguientes resultados:

Tabla SEQ Tabla \* ARABIC 3

Valores calculados de Densidad Seca in= -situ mediante la ecuación de correlación

Muestra No.<= o:p>

DS in-situ

kg/m³

(calculado)<= o:p>

Muestra No.<= o:p>

DS in-situ <= b>

kg/m³

(calculado)<= o:p>

Muestra No.<= o:p>

DS in-situ <= /span>kg/m³ (calculado)

1

965

11

1,380

21

1,389

2=

1,198

12

1,526

22

1,293

3

1,314

13

1,459

23

1,506

4=

1,236

14

1,556

24

1,498

5

1,410

15

1,512

25

1,432

6=

1,447

16

1,416

26

1,453

7

1,408

17

1,295

27

1,478

8=

1,352

18

653

28

1,178

9

1,353

19

1,438

29

1,530

10

1,293

20

1,308

30

1,571

La figura 2 representa los valores de Densidad Seca in-situ obtenidos mediante= el ensayo y los valores calculados con la ecuación de la correlación DCP - LL = – DS in-situ.

Figura <= b>2<= /p>

Comparación de valores de Densidad Seca in-situ obtenidos mediante ensayo y correlación

Correlación 2: Límite Líquido – Humedad Ópt= ima - CBR

Se utilizó el método de correlación de mínimos cuadrados con un número de 29 muestras, obteniendo un coeficiente de regresión de 0.85. La ecuación de correlación es

Figura <= b>3<= /p>

Correlación Límite Líquido – Humedad Óptima – CBR<= o:p>

Con esta correlación, se utilizaron los valores de Límite Líquido y Humedad Ópt= ima determinados mediante los ensayos de laboratorio, para calcular los valores de CBR media= nte la ecuación dada, obteniendo los siguientes resultados:

Tabla SEQ Tabla \* ARABIC 4

Valores calculados de CBR mediante la ecuación de correlación

Muestra No.<= o:p>	CBR % (calculado)<= o:p>	Muestra No.<= o:p>	CBR % (calculado)<= o:p>	Muestra No.<= o:p>	CBR % (calculado)<= o:p>
1	11.6	11	22.1	21	22.5
2=	12.2	12	43.0	22	22.9
3	16.2	13	46.1	23	41.5
4=	12.2	14	52.3	24	59.4
5	30.5	15	49.4	25	29.2
6=	30.3	16	31.0	26	30.8
7	19.0	17	27.6	27	36.4
8=	31.6	18	6.5	28	16.2
9	21.4	19	25.3	29	47.3
10	19.3	20	13.9	30	47.9

La figura 4 representa los valores de CBR obtenidos mediante el ensayo y los valores calculados con la ecuación de la correlación LL – HO – CBR.

Figura <= b>4<= /p>

Comparación de valores de CBR obtenidos mediante ensayo y correlación

Correlación 3: CBR – DCP

En el gráfico siguiente se puede observar la dispersión de 14 puntos con coordenadas DCP y CBR, de los valores obtenidos en campo y laboratorio, respectivamente. Se utilizó el método de correlación de mínimos cuadrados, encontrando un coeficiente de regresión potencial de 0.58. La ecuación de e= sta correlación es . La línea continua representa la correlación DCP-CBR de la norma ASTM D 6951.

Figura <= b>5<= /p>

Correlación 3: CBR – DCP

Discusión

En la correlación Límite Líquido – DCP - Densidad Seca In Situ (figura 1), se puede observar un coeficiente de correlación medio de 0.66. Los valores calculados de la densidad seca in situ (figura 2), tienen en general una aproximación a los valores de ensayo, para la mayoría de las muestras, sin embargo, para ciertas muestras existe una diferencia significativa. Teniend= o en cuenta que la correlación compara dos variables de campo, con una de laboratorio, en diferentes condiciones, la misma puede ayudar en la determinación aproximada de la Densidad Seca in-situ para el tipo de suelos estudiados.

La correlación Límite Líquido, Humedad Óptima, CBR, es alta con un coeficiente= de 0.85 (figura 3). Los valores de CBR calculados mediante esta correlación (figura 4), son muy aproximados a los valores determinados en laboratorio, = para la mayoría de las muestras. Es decir, para un determinado suelo granular, s= i se obtienen los valores de límite líquido y humedad óptima mediante los ensayos respectivos, se pueden determinar valores muy aproximados de CBR, que podrí= an ser utilizados en un estudio preliminar de suelos y diseńo de pavimentos.

La correlación entre el CBR y DCP (figura 5), utilizando 14 muestras presenta = un coeficiente de correlación potencial medio de 0.58. La curva de regresión o= btenida es paralela y semejante a la curva de la norma ASTM D 6951.

Conclusiones

· = Los suelos de subrasante estudiados, d= esde Tulcán hasta Riobamba a lo largo de la carretera Panamericana, son suelos granulares del tipo SM y ML (SUCS) y del tipo A–2-4, A-4 y A-5 (AASHTO). Es= tos suelos poseen una considerable cantidad de arena, finos de intermedia a baja plasticidad.

· = De las 30 muestras de suelo analizadas, poseen similares como su color, tipo o clasificación, humedad y densidad, s= in embargo, varían apreciablemente en otras propiedades como son límite líquido, límite= plástico, distribución granulométrica, CBR y DCP. <= /span>

· = Las correlaciones propuestas son aplic= ables únicamente a este tipo de suelos granulares, para los cuales su distribución granulométrica este en el rango indicado y mientras sus propiedades índice y mecánicas presenten valores cercanos a los obtenidos.

· = La baja correlación entre los valores = CBR y DCP puede deberse a que el CBR de laboratorio es obtenido en condiciones = controladas de humedad y densidad, mientras que el ensayo DCP es realizado en condicion= es naturales del suelo.

· = Para mejorar la correlación CBR vs. DC= P, para este tipo de suelos, se debería incrementar el número de muestras que tenga similares propiedades a las 14 muestras que fueron seleccionadas para mejorar la correlación.

· = Un método posible para obtener una bue= na correlación entre estos dos parámetros sería realizando los ensayos en condiciones idénticas, ya sea con moldeo de muestras gemelas o ensayos in-s= itu.

Referencias Bibliográficas

Al-Refeai, T., & Al-Suhaibani, A. (1996). Prediction of CBR Using Dynamic Cone Penetrometer. J. King Saud Univ., Vol. 9, Eng. Sci. (2), pp 191-204. https://reader.elsevier.com/reade= r/sd/pii/S1018363918306767?token=3DCD77A32BD1E89EB38FC25C579FF3D0E0A7952404= BAAB89A97F7B1E38EFB5EBE6D31339A123E0C2748FA1C9D63E481AF8&originRegion= =3Dus-east-1&originCreation=3D20221115134056

Arbianto, R., Yuono, T., & Gunarso. (2021). Comparison of California Bearing Ratio (CBR) Value Based on Cone Penetration Test (CPT) and Dynamic Cone Penetrometer (DCP). Journal of Advanced Civil and Environmental Engineering. Vol. 4, No. 2, pp 70-78. https://www.researchgate.net/publication/356173348_Compar= ison_of_California_Bearing_Ratio_CBR_Value_Based_on_Cone_Penetration_Test_C= PT_and_Dynamic_Cone_Penetrometer_DCP

American Society for Testing and Materials [ASTM].= (2009). ASTM D6951/D6951M–09 Standard test method for use of the dynamic cone penetrometer in shallow pavement applications. = Astm, (June), 1–7. https://doi.org/10.1520/D6951

Bermúdez, J., Vásquez, O., & Moncada, R. (2016). Validación del método de DCP aplicado en campo con relación al ensayo CBR en laboratorio. Laccei.Org. http://www.laccei.org/LACCEI2016-= SanJose/StudentPapers/SP61.pdf

Bowles, J. (1981). Manual de Laboratorio de Suelos en Ingeniería Civil. Ed. Mc. Graw Hill. Méx= ico.

Chen, D.-H., Wang, J.-N & Bilyeu, J. (2001). A= pplication of Dynamic Cone Penetrometer in Evaluation of Base and Subgrade Layers. = Transportation Research Record Journal of the Transportation Research Board 1764(1):1-10. = https://www.researchgate.net/publication/245559755_Application_of_Dyn= amic_Cone_Penetrometer_in_Evaluation_of_Base_and_Subgrade_Layers=

Encinares, E., & Encela, J. (2022). Prediction= of California Bearing Ratio (CBR) using Dynamic Cone Penetrometer (DCP) for So= ils from Second District in the Province of Sorsogon. United International Journal for Research & Technology, Vol. 3, Issue 05, pp 12-16. https://uijrt.com/articles/v3/i5/= UIJRTV3I50003.pdf

Feleke, G. G., & Araya, A. A. (2016). Predicti= on of CBR Using DCP for Local Subgrade Materials. International Conference = on transport and road research, 1–23. https://assets.publishing.service= .gov.uk/media/57a09dcc40f0b652dd001b8e/61280-GG_FelekeAA_Araya-PredictionCB= RUsingDCPLocalSubgradeMaterials-ICTRR2016-ReCAP.pdf

Gallegos, G. (2022). “Análisis de resultados de proyecto macro: correlación entre el = CBR, DCP, propiedades índice y mecánicas en suelos de los cantones Ambato, Quero, Tisaleo, Mocha y Cevallos”. Tesis UTA. Ambato. https://reposito= rio.uta.edu.ec/jspui/handle/123456789/35948

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Ntirenganya, N. (2020). Development of Correlation between Soaked CBR and In-Situ CBR Determined by DCP for Soil in the Southe= rn Province of Rwanda. International Journal of Scientific Research and Management (IJSRM), Vol. 8, Issue 01, pp 314-318. <= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; color:windowtext'>https://ijsrm.in/index.php/ijsrm/article/download/2512/19= 76/6757

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Wilches, F. J., Feria Diaz, J. J., & Hernández Ávil, J. R. (2018). Correlation between California Bearing Ratio (CBR)= and Dynamic Cone Penetrometer (DCP) for soil from Sincelejo city in Colombia. <= i>International Journal of Applied Engineering Research, 13(4), pp 2068–2071. http://www.ripublication.com/ijae= r18/ijaerv13n4_37.pdf

Zumrawi, M. M. E. (2014). Prediction of In-situ CB= R of Subgrade Cohesive Soils from Dynamic Cone Penetrometer and Soil Properties.= IACSIT International Journal of Engineering and Technology, Vol. 6, No. 5, pp 439-= 442. https://www.researchgate.net/publication/267630199_Predic= tion_of_In-situ_CBR_of_Subgrade_Cohesive_Soils_from_Dynamic_Cone_Penetromet= er_and_Soil_Properties

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ISSN: 2600-5859

Vol. 5 No. 4.1, pp. 45 – 59, noviembre – diciembre 2022

www= .concienciadigital.org

Megadatos Página = 45 | 44

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Muestra No.<= o:p>	Referencia	Retiene #4 <= o:p> %=	Pasa #200 %=	Límite Líqui= do %	Límite Plást= ico %	Índice Plást= ico %	SUCS	AASHTO<= /o:p>
1	Huaca	1	49	51.09	45.14	5.95	SM	A-5(4)
2=	San Gabriel<= o:p>	0=	60	43.23	34.88	8.35	ML	A-5(5)<= /o:p>
3	Bolívar	0	48	37.41	33.54	3.87	SM	A-4(3)
4=	El Ángel	0=	71	40.38	33.71	6.67	ML	A-4(7)<= /o:p>
5	Mira	5	60	31.71	24.61	7.10	ML	A-4(5)
6=	Chota	10	26	29.43	27.34	2.09	SM	A-2-4
7	Ibarra	4	47	32.45	25.79	6.66	SM	A-4(2)
8=	Atuntaqui	1=	56	33.31	29.90	3.41	ML	A-4(4)<= /o:p>
9	Otavalo	0	61	34.23	29.20	5.03	ML	A-4(5)
10	Cajas	6=	54	37.47	35.54	1.93	ML	A-4(3)<= /o:p>
11	Tabacundo	4	56	33.78	30.37	3.41	ML	A-4(4)
12	Cochasquí	1=	20	25.74	24.53	1.21	SM	A-2-4
13	Guayllabamba	4	26	28.90	30.23	-1.33	SM	A-2-4
14	Oyacoto=	10	35	23.26	23.16	0.10	SM	A-2-4
15	Pomasqui	1	27	25.00	23.12	1.88	SM	A-2-4
16	Cumbayá=	2=	38	30.15	26.16	3.99	SM	A-4(1)<= /o:p>
17	Cutuglahua	2	64	36.47	29.10	7.37	ML	A-4(6)
18	Alóag	1=	49	69.89	60.91	8.98	SM	A-5(5)<= /o:p>
19	Machachi	2	30	29.52	23.14	6.38	SM	A-2-4
20	Chasqui=	20	17	36.90	38.34	-1.44	SM	A-2-4
21	Lasso	3	25	32.00	27.65	4.35	SM	A-2-4
22	Latacunga	0=	57	35.73	36.83	-1.10	ML	A-4(4)<= /o:p>
23	Salcedo	2	29	26.04	26.78	-0.74	SM	A-2-4
24	Pansaleo	24	19	27.09	23.46	3.63	SM	A-2-4
25	Unamuncho	13	39	30.22	25.54	4.68	SM	A-4(1)
26	Ambato<= /o:p>	2=	48	28.92	22.68	6.24	SM	A-4(3)<= /o:p>
27	Huachi G.	3	51	27.72	24.44	3.28	ML	A-4(3)
28	Mocha	1=	47	43.76	41.25	2.51	SM	A-5(3)<= /o:p>
29	San Andrés	4	43	25.57	23.28	2.29	SM	A-4(2)
30	Riobamba	11	29	23.38	20.45	2.93	SM	A-2-4

Muestra No.<= o:p>	D.S in-situ kg/m³	D.S máxima kg/m³	Humedad Ópti= ma %	CBR<= /o:p> %=	DN mm/golpe
1	1,143	1,390	28.3	15.0	50.41
2=	1,183	1,393	28.9	7.8	7.28
3	1,195	1,485	24.8	16.7	3.26
4=	1,149	1,412	29.7	14.3	14.81
5	1,378	1,685	16.6	21.0	8.11
6=	1,716	1,783	17.1	30.7	10.82
7	1,293	1,501	23.2	19.4	2.51
8=	1,613	1,733	15.9	33.8	22.13
9	1,228	1,555	20.9	20.7	13.38
10	1,317	1,558	21.8	10.8	13.28
11	1,425	1,596	20.5	15.0	4.08
12	1,547	1,716	13.9	44.0	5.73
13	1,288	1,726	12.7	38.0	10.06
14	1,473	1,756	12.5	45.8	13.88
15	1,592	1,796	12.7	53.0	19.58
16	1,089	1,708	16.7	32.7	19.69
17	1,186	1,626	17.0	34.5	21.64
18	0,838	1,156	38.7	4.8	31.77
19	1,181	1,529	19.5	31.5	14.53
20	0,883	1,076	27.9	43.2	11.15
21	1,261	1,554	20.6	19.0	16.29
22	0,988	1,562	19.6	52.0	29.52
23	1,854	1,649	14.2	50.0	12.87
24	2,129	1,855	10.8	50.0	7.40
25	1,441	1,687	17.4	53.8	11.10
26	1,513	1,696	17.0	26.7	12.60
27	1,319	1,718	15.3	48.4	11.21
28	1,251	1,528	23.5	28.0	12.45
29	1,679	1,746	13.0	27.0	5.48
30	1,714	1,820	13.3	45.0	5.43

Muestra No.<= o:p>	DS in-situ kg/m³ (calculado)<= o:p>	Muestra No.<= o:p>	DS in-situ <= b> kg/m³ (calculado)<= o:p>	Muestra No.<= o:p>	DS in-situ <= /span>kg/m³ (calculado)
1	965	11	1,380	21	1,389
2=	1,198	12	1,526	22	1,293
3	1,314	13	1,459	23	1,506
4=	1,236	14	1,556	24	1,498
5	1,410	15	1,512	25	1,432
6=	1,447	16	1,416	26	1,453
7	1,408	17	1,295	27	1,478
8=	1,352	18	653	28	1,178
9	1,353	19	1,438	29	1,530
10	1,293	20	1,308	30	1,571