# Mechanical Characterization of Industrial Waste Materials as Mineral Fillers in Asphalt Mixes: Integrated Experimental and Machine Learning Analysis

^{1}

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## Abstract

**:**

## 1. Introduction

_{2}in the aggregate, for instance, what happens with marble fillers, provide great resistance against moisture-induced damage. Finally, Sherre and Liao [29] have verified that using hollow concrete block powder as a filler, in contrast to brick powder, extends asphalt mixtures’ service life by improving their moisture, rutting, and cracking resistance.

## 2. Materials and Experimental Investigation

#### 2.1. Materials

#### 2.1.1. Aggregate and Bitumen Properties

#### 2.1.2. Mineral Filler

#### 2.2. Designing and Testing of Asphalt Concrete

#### 2.2.1. Sample Preparation and Determination of Marshall and Volumetric Properties

#### 2.2.2. Rutting Resistance

#### 2.2.3. Cracking Resistance

#### 2.2.4. Moisture Susceptibility

#### 2.2.5. Cantabro Abrasion Loss Test

#### 2.3. Theory of Machine Learning Modeling

#### 2.3.1. Artificial and Shallow Neural Networks

#### 2.3.2. SNN Optimization

**,**it is possible to define a function $F\left(\widehat{\mathit{y}},\mathit{y}\right)$, commonly called the performance function or the loss function, which helps with determining the adjustments to be made to the weights and biases matrix $\mathit{W}$, so that a subsequent iteration can produce better results. These adjustments are indicated with the name of learning rule. The analytical expression of the one employed in this study is the following:

^{®}ANN Toolbox. In fact,$E$ has been set equal to 1000, ${\mu}_{\mathrm{inc}}$ to 10, ${\mu}_{\mathrm{dec}}$ to 0.1, ${\mu}_{\mathrm{max}}$ to 1.0e+10, and the starting $\mu $ equal to 0.001.

#### 2.3.3. k-Fold Cross-Validation

#### 2.3.4. Data Augmentation

^{®}: it consists in performing a cubic interpolation that produces piecewise polynomials characterized by continuous first-order derivatives. In other words, a generic piecewise cubic polynomial $P\left(q\right)$ with $q\in \left({q}_{m},{q}_{m+1}\right)$, an interval of input nodes $q$ to which correspond the values $p$ of the function to be interpolated, has to satisfy the following four interpolation conditions, two on the function values and two on the (unknown) derivative values:

## 3. Results and Discussion

#### 3.1. Performance of Modified Asphalt Mixes

#### 3.1.1. Marshall and Volumetric Properties

#### 3.1.2. Cracking Resistance

#### 3.1.3. Moisture Susceptibility

#### 3.1.4. Abrasion Loss

#### 3.2. Numerical Discussion

_{OPT}. Nevertheless, the better accuracy requires more computational power; the use of $N=24$ neurons in the hidden layer makes the structure complex and the calculation slightly more onerous than the best ELU network solution. In addition, it should be noted that the $\delta =196$ parameters (weights and biases) defining the SNN

_{OPT}contribute $ENP=95.27\%$ to define the model’s predictive capacity, whereas this value is reduced to $90.84\%$ in the case of ELU function use. Once the optimal solutions for each activation function have been reached, the ENP value begins to gradually decrease (purple dots in Figure 13), indicating that adding any new parameters to the model does not result in a significant improvement in its predictive accuracy, but rather forces the regularization algorithm to minimize the effect of the added parameters. Conversely, it can be observed that the solutions preceding the optimal SNN models (red dots in Figure 12) are progressively improving in performance as the number of hidden neurons increases.

_{OPT}has very good generalization capability (Figure 12), as the coefficient ${R}_{cv}=0.9967$ is so high as to be representative of an almost perfect fit to the experimental data (and, consequently, the normalized $MS{E}_{cv}=0.0078$ is actually small). This is not to say that the model is overfitted if the trend of the data requires passing over experimentally sampled points. Figure 14 shows the experimental observations (the dots) and the SNN

_{OPT}predictions of the four mechanical parameters (the curves) for each of the eight filler types considered in the study (indicated by eight different colors), according to the filler content vs. air voids relationship curves presented in Figure 5. The analysis of these graphs qualitatively confirms what was pointed out in the experimental discussion: the SNN

_{OPT}model is a smooth regression of the experimental observations and does not have any excessive fluctuations or strange oscillations typical of the overfitting phenomenon. Indeed, it is able to preserve and properly reproduce the physical behavior of asphalt concretes. In particular, as the percentage of filler by volume of the mix increases (and consequently, according to the curves in Figure 4, as the air void content decreases), all mechanical features are improved, whatever the type of filler is used.

## 4. Conclusions

- The chemical and physical properties of silica fume (SF), limestone dust (LSD), stone dust (SD), rice husk ash (RHA), fly ash (FA), brick dust (BD), and marble dust (MD) have been assessed, and it has been observed that all the fillers follow the required requisites of the Indian Standard. Therefore, these fillers can be used in asphalt concrete;
- The mechanical strength and moisture susceptibility tests on the waste-modified asphalt mixtures have shown satisfactory results with respect to the acceptance requisites, as per the MoRTH. Therefore, these mineral fillers can be effectively used for field applications. The acceptance requisite for mixtures made with modified bitumen (MS higher than 13 kN) is fully satisfied if the filler content is raised from 5.5% to 8.5% in case of LSD and SF, demonstrating the significant improvement to the mechanical response of the asphalt mixes given by the filler. It has been observed that LSD- and SF-modified asphalt mixtures have demonstrated superior performance, compared to the OPC-modified asphalt concretes;
- The LSD and SF can be identified as the best alternatives to the OPC in asphalt concrete, since the performances of these mixes were better than those of the OPC-modified asphalt concretes. The MS of SF (13.98 kN) and LSD (13.86 kN) was slightly higher, with respect to the asphalt mixtures made with OPC (13.74 kN). The calcium-based mineral filler performed extremely well due to its higher moisture resistance, though the other filler-modified mixtures also fulfilled the requirements of the MoRTH, since ITSR values greater than 75%, and MS values greater than 10 kN have been observed;
- Shallow neural networks have been successfully used to develop a predictive model of the main empirical and rational mechanical characteristics of ACs prepared with different filler types, namely, Marshall stability (kN), Marshall quotient (kN/mm), indirect tensile strength (kPa), and indirect tensile strength ratio (%);
- The results obtained from the implementation of the k-folds resampling and MAKIMA data-augmentation methods in the SNN’s learning process were fully satisfactory considering the small number of experimental observations available. The optimal SNN for the modeling problem in question receives as input a three-component features vector, the air voids content (%), the filler content (% by volume of the mix), along with the categorical variable distinguishing the filler type; such features are processed by a twenty-four-neuron hidden layer characterized by the hyperbolic tangent activation function;
- The optimal SNN model is a smooth regression of the experimental observations and is able to properly reproduce the physical behavior of asphalt concretes: as the percentage of filler by volume of the mix increases and, consequently, as the air voids content decreases, all mechanical features are improved, regardless of the type of filler used;
- The optimal model developed in this study admits solutions only along the univocal relationship existing between the filler content and air voids, as the type and content of bitumen, as well as the type of coarse aggregate, have been kept constant in the preparation of the investigated mixtures. Therefore, this SNN represents a modeling experience that, for the obtained results, encourages the development of an extensive model that can be applied to other types of mixtures and allow the optimal filler contents for each asphalt concrete in question to be properly identified.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 6.**Voids in the mineral aggregate of the asphalt mixes with waste and conventional mineral fillers.

**Figure 7.**Voids filled with bitumen of the asphalt mixes with waste and conventional mineral fillers.

**Figure 9.**Marshall quotient (kN/mm) of the asphalt mixes with waste and conventional mineral fillers.

**Figure 10.**Indirect tensile strength of the asphalt mixes with waste and conventional mineral fillers.

**Figure 11.**Indirect tensile strength ratio of the asphalt mixes with waste and conventional mineral fillers.

**Figure 13.**Grid search results with (

**a**) ELU as the activation function and with (

**b**) TanH as the activation function. In these plots, the results for the correlation coefficient ${R}_{cv}$ are represented with blue dots, the mean squared error $MS{E}_{cv}$ with in orange dots, and the effective number of parameters $ENP$, obtained by testing different possible neural configurations, in purple dots. The red dots identify the optimal solutions for each activation function considered.

**Figure 14.**Model prediction vs. experimentally observed target for each filler type: (

**a**) MS, (

**b**) MQ, (

**c**) ITS, and (

**d**) ITSR. These plots show the fit of the model to the experimental data and allow the modeling result to be interpreted in physical terms, to verify its correspondence to real-world scenarios.

Material | Test Parameters | Specified Limit (MoRTH) | Test Results | Test Method |
---|---|---|---|---|

Aggregate | Cleanliness (dust) (%) | Max 5 % | 3 | IS 2386 Part I |

Bulk specific gravity (g/cm3) | 2–3 | 2.68 | IS 2386 Part III | |

Percent wear by Los Angeles abrasion (%) | Max 35 % | 10.6 | IS 2386 Part IV | |

Soundness loss by sodium sulphate solution (%) | Max 12% | 3.4 | IS 2386 Part V | |

Soundness loss by magnesium sulphate solution (%) | Max 18% | 3.7 | IS 2386 Part V | |

Flakiness and elongation index (%) | Max 35% | IS 2386 Part I | ||

20 mm | 27.93 | |||

10 mm | 32.13 | |||

Impact strength (%) | Max 27% | IS 2386 Part IV | ||

20 mm | 4.15 | |||

10 mm | 5.91 | |||

Water absorption (%) | Max 2% | 1.67 | IS 2386 Part III | |

Bitumen | Absolute viscosity at 60 °C (poises) | 2400–3600 | 2855 | IS 1206 (P-2) |

Kinematic viscosity at 135 °C (cSt), Min. | 350 | 392 | IS 1206 (P-3) | |

Flash point Cleveland open cup, (°C), Min. | 250 | 304 | IS 1448 (P-69) | |

Penetration at 25 °C, 100 gm, 5 sec, (1/10 mm), Min | 45 | 49 | IS 1203 | |

Softening point (R&B), (°C), Min | 47 | 48 | IS 1205 | |

Matter soluble in trichloroethylene, (% by mass), Min. | 99 | 99.45 | IS 1216 | |

Viscosity ratio at 60 °C, Max | 4.0 | 1.3 | IS 1206 (P-2) | |

Ductility at 25 °C, (cm) after TFOT min. | 40 | 75 | IS 1208 | |

Specific gravity (gm/cc) | 0.97–1.02 | 0.987 | IS 1202 |

Property | Type of Mineral Filler | |||||||
---|---|---|---|---|---|---|---|---|

OPC | LSD | FA | RHA | SD | MD | BPD | SF | |

Specific gravity (g/cm^{3}) | 3.04 | 2.65 | 2.32 | 2.02 | 2.69 | 2.695 | 2.56 | 2.2 |

MBV (g/kg) | 3 | 3.75 | 3.86 | 4.72 | 3.67 | 4.45 | 6.25 | 3.85 |

German filler (g) | 85 | 97 | 75 | 65 | 85 | 70 | 40 | 94 |

Fineness modulus (FM) | 4.96 | 3.03 | 3.77 | 3.21 | 5.38 | 2.12 | 5.17 | 1.96 |

Surface area (m^{2}/g) | 1.75 | 2.70 | 2.193 | 2.31 | 2.701 | 4.372 | 2.688 | 16.45 |

pH | 12.9 | 10.22 | 7.3 | 10.86 | 12.57 | 8.5 | 8.67 | 6.98 |

SiO_{2} (%) | 21.43 | 0.48 | 48.24 | 89.67 | 82.37 | 0.6 | 39.55 | 93.5 |

CaO (%) | 66.58 | 96.57 | 13.4 | 1.88 | 2.79 | 55.6 | 12.88 | 0.89 |

Al_{2}O_{3}(%) | 3.01 | 0.41 | 24.15 | 1.62 | 8.23 | 0.4 | 15.71 | 0.08 |

MgO(%) | 1.39 | 0.46 | 1.46 | 0.97 | 1.47 | 0.1 | 3.29 | 0.82 |

Fe_{2}O_{3}(%) | 4.68 | 0.32 | 6.48 | 1.06 | 5.27 | 0.2 | 14.05 | 0.5 |

Particle shape | Granules and sub-angular particles | Granular particles | Rounded | Honeycombed | Angular particles e | Sub-angular | Sub-angular particles | Spherically shaped |

No. | Name of Equipment | Specification |
---|---|---|

1. | Digital Marshall Stability Apparatus | Ref. standards—ASTMD 1559, ASTM D6927-06 Capacity—100 kN single speed Sample size—4″ and 6″ dia Load cell: 100 kN LVDT: 50 mm Maximum vertical clearance—470 mm Minimum vertical clearance—250 mm Horizontal clearance—265 mm Platen diameter—133 mm Platen travel—25 mm Platen speed—50.8 mm/min Rated power—375 W Dimension (l × w × h)—550 × 400 × 870 mm |

2. | Automatic Compactor for Bituminous Mixes | Ref. standards: ASTM D 5581:1996, ASTM D 6926-04 Weight of hammer—4.5 kg (4″ sample dia) and 10.2 kg (6″sample dia) Sample ejector: 4″ and 6″ sample dia Falling height: 457 mm Suitable for operation—440 V and 50 Hz Power supply—three-phase AC supply |

3. | Indirect Tensile Strength Test Machine | Ref. standard: ASTM D6931 Ram stroke—400 mm Ram speed range—50 to 70 mm/min Load cell—250 kN Deformation transducer—400 mm Clearance between columns—530 mm Electrical supply—three-phase 415 Volts 50 Hz Dimensions (W × D × H)—2100 × 616 × 2111 mm Working space required (W × D × H)—2300 × 1616 × 2300 mm |

4 | Los Angeles Abrasion Machine | Ref. Standard: AASHTO T 96, ASTM C535 Revolutions per minute—30–33 Drum diameter—700 mm Inside height—500 mm Electrical—220 V/50 Hz Product dimensions (W × D × H)—965 × 1016 × 1181 mm |

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## Share and Cite

**MDPI and ACS Style**

Tiwari, N.; Baldo, N.; Satyam, N.; Miani, M.
Mechanical Characterization of Industrial Waste Materials as Mineral Fillers in Asphalt Mixes: Integrated Experimental and Machine Learning Analysis. *Sustainability* **2022**, *14*, 5946.
https://doi.org/10.3390/su14105946

**AMA Style**

Tiwari N, Baldo N, Satyam N, Miani M.
Mechanical Characterization of Industrial Waste Materials as Mineral Fillers in Asphalt Mixes: Integrated Experimental and Machine Learning Analysis. *Sustainability*. 2022; 14(10):5946.
https://doi.org/10.3390/su14105946

**Chicago/Turabian Style**

Tiwari, Nitin, Nicola Baldo, Neelima Satyam, and Matteo Miani.
2022. "Mechanical Characterization of Industrial Waste Materials as Mineral Fillers in Asphalt Mixes: Integrated Experimental and Machine Learning Analysis" *Sustainability* 14, no. 10: 5946.
https://doi.org/10.3390/su14105946