# Optimization of Compaction Quality Control in the Core of Random Fillings within Linear Infrastructures: Application to Metamorphic Slate Fillings

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

^{2}and 1 m

^{3}of volume. So, it is complex to obtain gradings weighing fractions of different aggregates. For Teijón el al. [4], the nuclear methods for the obtention of density and humidity in situ are not adequate in random fillings for a tested thickness of 300mm when the layer thicknesses are usually 600mm. Also, the particle sizes reduce the significance of the test. The density by substitution methods such as sand are not correct, the high hollow introduces errors, limiting its application to 50mm. For larger sizes the vibration table is recommended.

_{m}). This value is called the compaction degree index.

^{a}(artificial lunar regolite), obtaining relationships between the density of the soil or lunar regolite, the ripple force, and the spacing and number of scarifiers. The instrumentation allows a high-resolution mapping of the density of the raked site, providing an in situ calibration of the ground by remote control from the Earth. For Wu and Wang [14], the effect of the time between the layers on field compaction must be considered in the construction of filler. For longer surface exposures, moisture tends to evaporate, and test results change. With the Clegg soil impact test hammer, compacted Xiangshan sand was practical for dry density measurement. The force of compacted sand and compaction effort correlated well with the soil impact test hammer. The main factors influencing the compacted Xiangshan sand were moisture and degree of compaction. Lower compaction effort results in lower soil strength as moisture content increases. The stability of the embankments depends on the quality of the compaction of the fill. Nondestructive testing techniques have more advantages than that of conventional field density tests. Therefore, the use of nondestructive testing techniques in fill monitoring seems interesting in geotechnical applications. Using the Clegg impact tester, impact (Iv) values varying in compaction effort, moisture content, and density were observed in the laboratory. The variations of Iv with moisture are equal to the moisture-density ratio. The Iv has a strong relationship, for each compaction effort, with the moisture-density ratio. With a simple moisture test, the dry density can be predicted using the Iv values. This allows efficient quality control compaction.

## 2. Material and Methods

#### 2.1. Material

^{3}rock digging approximately. Table 2 provides examples of the tests that were conducted on the slate alluvial material during excavation, with the last row showing average values.

- UCS: unconfined compressive strength [kp/cm
^{2}]. - RQD: rock quality designation—a quality index proposed by Deere. It is the relation of the percentages between the sum of the recovered pieces from the borehole with length higher than 10 cm and the total length drilled in the maneuver. This length depends on the compactness of the ground, and in this investigation, it was basically between 1.5 and 3.0 m.
- RMR: rock mass rainting—quality index of the rock, which was calculated based on other parameters such as unconfined compressive strength (UCS), the RQD evaluated previously, the spacing, condition and orientation of the discontinuities, and, lastly, the presence of water.

#### 2.2. Methods

## 3. Results

#### 3.1. Relation Wheel-Tracking—Topographic Settlement Tests

^{2}= 0.710 means a variance percentage of 71.0%. The standard error is 0.1475 mm.

^{2}= 0.710

#### 3.2. Relation Wheel-Tracking Test—First PBT Modulus

^{2}= 0.980 yields a variance of 98%. The standard error is only 4.1934 MPa.

_{1}= 129.468 − 26.921 h R

^{2}= 0.980

_{1}≤ 110] and [0.5 ≤ h ≤ 4.5].

#### 3.3. Relation Topographic Settlement Test—First PBT Modulus

_{1}= 169.243 − 24.549 s R

^{2}= 0.925

_{1}≤ 100] and [3.0 ≤ s ≤ 6.0] intervals.

#### 3.4. Relation Topographic Settlement Test—Second PBT Modulus

^{2}= 0.990. There is low dispersion.

_{2}= 403.329 − 48.108 s R

^{2}= 0.985

_{2}≤ 240] and [3.5 ≤ s ≤ 6.0].

#### 3.5. Significance Matrix

#### 3.6. Discussion

- Obtaining the compaction degree index in a length five times longer than the initial one, with twice as many measurements for the same section of all-in-one backfill;
- Reduction of levelling errors by having a fixed point on the metal levelling spike and not on the ground, guaranteeing millimetric precision;
- Increased performance by reducing test times, with the first measurement being made on the picks without the need to move the heavy metal support used in the measurement;
- The possible dynamic effects of acceleration/braking of the truck are minimized by increasing the distance travelled and increasing the time taken for the truck to establish a constant speed when passing over the cutting tools;
- With two measurements per profile, a more complete check of the all-one fill section is made than with a single point. By measuring in two parallel and independent tracks, any deficiencies in one track are corrected. In addition, second-order effects such as the weight of the driver or the fuel tank are no longer considered in the test;

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## The contributions of the research are:

- The nuclear methods have a low efficiency, limited by a maximum test thickness of 30 cm and the high variability of the materials;
- The PBT (ɸ 300 mm) provides unreliable results in the core of random fillings, with maximum sizes up to 500 mm. This research has demonstrated optimal control using PBT (ɸ 600 mm);
- The PBT (ɸ 600 mm) is proposed as the most representative test to define the degree of compaction in the new control method on core of slate random fillings. As this test is strongly associated with surface moisture, it should be carried out in the same area of validity as the optimum moisture obtained in the modified Proctor;
- New procedures for topographical settlement control and wheel impression tests were applied with optimal results to the core of random fillings formed by slates with maximum layer thicknesses of 800 mm;
- Statistical correlations were found between different compaction tests, which made it possible to eliminate redundant tests, thus optimizing quality control and construction procedures;
- The wheel tracking test can be deduced from the adjustment model for values between 1.5 ≤ h ≤ 4 mm. The limitations of the nuclear methods made it impossible to relate to other tests. Finally, the topographic seat control can be replaced for values of the PBT modules between 20 ≤ Ev
_{1}≤ 100 and 140 ≤ Ev_{2}≤ 240; - In the core of random fillings, including foundations and shoulders, which are formed by slates laid in layers with a maximum thickness of 800 mm, considering all the factors descripted in this research and summary in the discussion and conclusions, it is proposed as a quality control of the compaction to carry out PBT tests (ɸ 600 mm) and the in situ determination of density and moisture content by nuclear methods.

## References

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Checklist of Tests | Limitations | Spanish Procedures |
---|---|---|

topographic settlements | no reference values | on test sections |

automatic online monitoring | strong influence on human behavior | not applied |

pit gradings | not practical and unsafe | on test sections |

wheel track testing ( UNE 103407) | minor than 5 mm (works in normal conditions) | in compaction batches |

plate bearing test (UNE 103808) | diameter of the element 5 times the maximum size | requires the diameter of the element to be 5 times the maximum size |

nuclear density gauging (UNE 103900) | particle dimensions | not recommended in compaction batches only correlated with other tests |

modified proctor (UNE 103501) | replacement 70% fines | usually reference to maximum density and optimum moisture |

sand method (UNE 103501) | maximum size < 50mm | in compaction batches |

Ref. | # 100 (mm) | # 20 (mm) | # 2 (mm) | #0.40 (mm) | #0.075 (mm) | Liquid Limit (LL) | Plastic Limit (PL) | Plasticity Index (PI) | Membership USGS index | Dry Density (g/cm^{3}) | Humidity (%) | CBR |
---|---|---|---|---|---|---|---|---|---|---|---|---|

CC-017 | 100.0 | 56.0 | 29.0 | 20.0 | 14.5 | 29.5 | 21.4 | 8.1 | 0.900 | 2.14 | 6.7 | 25.8 |

CC-014 | 100.0 | 54.0 | 22.0 | 16.0 | 13.3 | 31.8 | 24.1 | 7.6 | 0.900 | 2.14 | 6.8 | 14.0 |

CC-015 | 100.0 | 40.0 | 17.0 | 14.0 | 11.5 | 31.9 | 19.4 | 12.5 | 0.900 | 2.05 | 8.8 | 9.3 |

I-09030/04 | 100.0 | 66.0 | 41.0 | 28.0 | 20.6 | 35.0 | 24.3 | 10.7 | 0.600 | 2.06 | 5.3 | 21.1 |

CC-011 | 100.0 | 89.0 | 53.0 | 46.0 | 38.4 | 30.3 | 23.4 | 6.9 | 0.800 | 2.10 | 7.5 | 6.6 |

CC-027 | 100.0 | 72.0 | 47.0 | 35.0 | 28.9 | 28.1 | 21.7 | 6.4 | 0.800 | 2.10 | 10.0 | 25.8 |

Averages | 100.0 | 64.0 | 35.7 | 26.3 | 21.1 | 31.9 | 22.3 | 9.9 | 0.817 | 2.05 | 8.9 | 15.5 |

Depth (m) | Lithologhy | Weathering Grade | UCS (kp/cm^{2}) | RQD (%) | Diaclase Spacing (mm) | Water Freatic | RMR |
---|---|---|---|---|---|---|---|

3.40–7.10 | slate | IV–V | 7.40 | 90.00 | 0.33 | almost dry | 53 |

7.10–14.60 | slate | III–IV | 100.90 | 85.00 | 0.33 | almost dry | 55 |

14.60–16.00 | slate | III | 194.00 | 90.00 | 0.33 | almost dry | 55 |

2.20–4.30 | shale | III–IV | 30.00 | 0.00 | 0.30 | slightly wet | 22 |

4.30–9.00 | shale | III–IV | 122.00 | 21.00 | 0.13 | slightly wet | 38 |

9.00–10.00 | shale | III–IV | 30.00 | 0.00 | 0.03 | slightly wet | 22 |

3.50–5.80 | slate | IV–V | 104.70 | 10.00 | 0.03 | almost dry | 34 |

5.80–7.80 | slate | III–IV | 104.70 | 50.00 | 0.40 | almost dry | 46 |

7.80–8.55 | grauwacke | III | 44.60 | 50.00 | 0.40 | almost dry | 46 |

laboratory | 2250 nuclear methods | UNE 103900 [22] |

425 modified Proctor | UNE 103501 [23] | |

field | 75 wheel impression | UNE 103407 [24] |

75 topographic settlements | PG-3 [3] | |

75 PBT | UNE 103808 [25] |

Density | Settlement | Modulus | Plate Bearing Test | ||
---|---|---|---|---|---|

Degree of Compaction (%) | h (mm) | s (mm) | Ev_{1} (MPa) | Ev_{2} (MPa) | k (Ev_{2}/Ev_{1}) |

95.0 | ≤4.0 | ≤4.0 | ≥30.0 | --- | <3.0 |

--- not required |

Summary Model | |||
---|---|---|---|

R | R^{2} | R^{2} Fit | Standard Error |

0.843 ^{a} | 0.710 | 0.637 | 0.1475 |

^{a}Predictors: constant, h (mm).

ANOVA ^{a} | |||||
---|---|---|---|---|---|

Model | Sum of Squares | Degrees of Freedom | Quadratic Average | F | Sig. |

regression | 0.213 | 1 | 0.213 | 9.786 | 0.035 ^{b} |

sampling error | 0.087 | 4 | 0.022 | ||

total | 0.3 | 5 |

^{a}dependent variable: s (mm)

^{b}predictors: (constant), h (mm.)

Coefficients ^{a} | |||||
---|---|---|---|---|---|

Model | Nonstandard Coefficients | Standard Coefficients | t | Sig. | |

B | Standard Error | Beta | |||

(constant) | 2.446 | 0.218 | 11.237 | 0.000 | |

h (mm) | 0.257 | 0.082 | 0.843 | 3.128 | 0.035 |

^{a}dependent variable: s (mm).

Summary Model | |||
---|---|---|---|

R | R^{2} | R^{2} Adjusted | Standard Error |

0.990 ^{a} | 0.980 | 0.975 | 4.1934 |

^{a}Predictors: constant, h (mm.)

ANOVA ^{a} | |||||
---|---|---|---|---|---|

Model | Sum of Squares | Degrees of Freedom | Quadratic Average | F | Sig. |

regression | 3513.855 | 1 | 3513.855 | 199.826 | 0.000 ^{b} |

sampling error | 70.338 | 4 | 17.585 | ||

total | 3584.1932 | 5 |

^{a}dependent variable: Ev

_{1}(MPa)

^{b}predictors: (constant), h (mm)

Coefficients ^{a} | |||||
---|---|---|---|---|---|

Model | Nonstandard Coefficients | Standard Coefficients | t | Sig. | |

B | Standard Error | Beta | |||

(constant) | 129.468 | 3.974 | 32.576 | 0.000 | |

h (mm) | −26.291 | 1.904 | −0.990 | −14.136 | 0.000 |

^{a}dependent variable: Ev

_{1}(MPa.)

Summary Model | |||
---|---|---|---|

R | R^{2} | R^{2} Adjusted | Standard Error |

0.962 ^{a} | 0.925 | 0.9 | 7.1343 |

^{a}Predictors: constant, s (mm.)

ANOVA ^{a} | |||||
---|---|---|---|---|---|

Model | Sum of Squares | Degrees of Freedom | Quadratic Average | F | Sig. |

regression | 1875.433 | 1 | 1875.433 | 36.847 | 0.009 ^{b} |

sampling error | 152.695 | 3 | 50.898 | ||

Total | 2028.128 | 4 |

^{a}dependent variable: Ev

_{1}(mm);

^{b}predictors: (constant), s (mm).

Coefficients ^{a} | |||||
---|---|---|---|---|---|

Model | Non-Standard Coefficients | Standard Coefficients | t | Sig. | |

B | Standard Error | Beta | |||

(constant) | 169.243 | 17.124 | 9.884 | 0.002 | |

h (mm) | −24.549 | 4.044 | −0.962 | −6.070 | 0.009 |

^{a}dependent variable: Ev

_{1}(MPa).

Summary Model | |||
---|---|---|---|

R | R^{2} | R^{2} Adjusted | Standard Error |

0.995 ^{a} | 0.990 | 0.985 | 4.5260 |

^{a}Predictors: constant, s (mm).

ANOVA ^{a} | |||||
---|---|---|---|---|---|

Model | Sum of Squares | Degrees of Freedom | Quadratic Average | F | Sig. |

regression | 3951.860 | 1 | 3951.860 | 19.251 | 0.005 ^{b} |

sampling error | 40.970 | 2 | 20.845 | ||

total | 3992.830 | 3 |

^{a}dependent variable: Ev

_{2}(mm);

^{b}predictors: (constant), s (mm.)

Coefficients ^{a} | |||||
---|---|---|---|---|---|

Model | Nonstandard Coefficients | Standard Coefficients | t | Sig. | |

B | Standard Error | Beta | |||

(constant) | 403.329 | 15.493 | 26.420 | 0.001 | |

s (mm) | −48.108 | 3.464 | −0.995 | −13.889 | 0.005 |

^{a}dependent variable: Ev

_{2}(MPa).

Determination Coefficients (R^{2}) | ||||||
---|---|---|---|---|---|---|

d (g/cm^{3}) | h (mm) | s (mm) | Ev_{1} (MPa) | Ev_{2} (MPa) | k (Ev_{2}/Ev_{1}) | |

d (g/cm^{3}) | --- | |||||

h (mm) | ns | --- | ||||

s (mm) | ns | 0.710 | --- | |||

Ev_{1} (MPa) | ns | 0.874 | 0.925 | --- | ||

Ev_{2} (MPa) | (*) | ns | 0.990 | ns | --- | |

k (Ev_{2}/Ev_{1}) | (*) | ns | (*) | (*) | Ns | --- |

Student t Test (t) | ||||||
---|---|---|---|---|---|---|

d (g/cm^{3}) | h (mm) | s (mm) | Ev_{1} (MPa) | Ev_{2} (MPa) | k | |

d (g/cm^{3}) | --- | |||||

h (mm) | ns | --- | ||||

s (mm) | ns | 3.128 | --- | |||

Ev_{1} (MPa) | ns | −14.136 | −6.070 | --- | ||

Ev_{2} (MPa) | (*) | ns | −13.890 | ns | --- | |

K | (*) | ns | (*) | (*) | ns | --- |

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**MDPI and ACS Style**

Teijón-López-Zuazo, E.; Vega-Zamanillo, Á.; Calzada-Pérez, M.Á.; Juli-Gándara, L. Optimization of Compaction Quality Control in the Core of Random Fillings within Linear Infrastructures: Application to Metamorphic Slate Fillings. *Sustainability* **2021**, *13*, 10957.
https://doi.org/10.3390/su131910957

**AMA Style**

Teijón-López-Zuazo E, Vega-Zamanillo Á, Calzada-Pérez MÁ, Juli-Gándara L. Optimization of Compaction Quality Control in the Core of Random Fillings within Linear Infrastructures: Application to Metamorphic Slate Fillings. *Sustainability*. 2021; 13(19):10957.
https://doi.org/10.3390/su131910957

**Chicago/Turabian Style**

Teijón-López-Zuazo, Evelio, Ángel Vega-Zamanillo, Miguel Ángel Calzada-Pérez, and Luis Juli-Gándara. 2021. "Optimization of Compaction Quality Control in the Core of Random Fillings within Linear Infrastructures: Application to Metamorphic Slate Fillings" *Sustainability* 13, no. 19: 10957.
https://doi.org/10.3390/su131910957