Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas
Abstract
:1. Introduction
- The DWGR data were collected from 160 buildings, and a dataset was constructed by applying data preprocessing for the raw data.
- ANN, SVM, LR, KNN, and RF were considered ML algorithms for the development of the DWGR prediction models.
- The DWGR prediction models were developed by deriving the optimal HPs for each ML algorithm.
- The leave one out cross-validation (LOOCV) technique was used for model validation. The mean squared error (MSE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) were used as the statistical metrics for evaluating the model performance.
- Optimal ML models for the DWGR prediction were proposed by evaluating the performance of the developed models.
2. Materials and Methods
2.1. Data Collection and Preprocessing
2.2. Applied Machine Learning Algorithms
2.2.1. Artificial Neural Network
2.2.2. Support Vector Machine
2.2.3. Linear Regression
2.2.4. K-Nearest Neighbors
2.2.5. Random Forest
2.3. Hyper-Parameter Tuning
2.4. Model Validation and Evaluation
2.4.1. Model Validation
2.4.2. Model Uncertainty Analysis
3. Results and Discussion
3.1. Performance Results
3.2. Comparison of Prediction Results and Uncertainty Analysis of Best Models
3.3. Importance of Input Variables
4. Discussion and Recommendations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
References
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Category | Number of Buildings | DWGR (kg·m−2) | GFA (m2) | ||||
---|---|---|---|---|---|---|---|
Total | Min | Mean | Max | ||||
Location | Project A | 81 | 60,072.6 | 534.8 | 741.6 | 1137.3 | 8301.0 |
Project B | 79 | 97,876.3 | 795.2 | 1238.9 | 1729.1 | 13,627.3 | |
Usage | Residential | 135 | 126,624.9 | 534.8 | 938.0 | 1629.4 | 16,994.4 |
Residential & commercial (Re/Co) | 25 | 31,324.0 | 750.9 | 1253.0 | 1729.1 | 4933.8 | |
Structure | Reinforced Concrete | 35 | 50,062.4 | 795.2 | 1430.4 | 1637.0 | 8652.1 |
Concrete block (Con_blo) | 81 | 69,225.6 | 534.8 | 854.6 | 1180.5 | 7539.8 | |
Concrete brick (Con_bri) | 15 | 21,782.2 | 717.0 | 1452.1 | 1729.1 | 2500.4 | |
Wood | 29 | 21,905.8 | 590.5 | 755.4 | 883.0 | 3236.0 | |
Wall type | Brick | 32 | 31,870.0 | 590.5 | 995.9 | 1729.1 | 4556.0 |
Block | 121 | 121,096.4 | 534.8 | 1000.8 | 1637.0 | 16,579.0 | |
Soil | 7 | 4982.5 | 668.0 | 711.8 | 759.8 | 4556.0 | |
Roof type | Slab | 37 | 45,448.0 | 717.0 | 1228.3 | 1729.1 | 7003.5 |
Slab roofing tile (Slab/R.t) | 33 | 40,035.1 | 931.0 | 1213.2 | 1637.0 | 5080.8 | |
Slab & slate | 3 | 3930.9 | 813.5 | 1310.3 | 1614.8 | 719.6 | |
Slate | 13 | 7796.1 | 534.8 | 599.7 | 681.8 | 1384.9 | |
Roofing tile (R.t) | 74 | 60,738.8 | 580.0 | 820.8 | 1576.5 | 7739.5 |
Machine Learning Algorithms | Hyper Parameters | |||
---|---|---|---|---|
Title | Tested Values | Selected | ||
ANN | Identity | number of neurons | Range (10, 200, step = 10) | 10 |
iteration | Range (10, 1000, step = 10) | 50 | ||
regularization | Range (0.001, 1000) | 1000 | ||
Logistic | number of neurons | Range (10, 200, step = 10) | 40 | |
iteration | Range (10, 1000, step = 10) | 400 | ||
regularization | Range (0.001, 1000) | 20 | ||
Relu | number of neurons | Range (10, 200, step = 10) | 140 | |
iteration | Range (10, 1000, step = 10) | 200 | ||
regularization | Range (0.001, 1000) | 600 | ||
Tanh | number of neurons | Range (10, 200, step = 10) | 90 | |
iteration | Range (10, 1000, step = 10) | 1000 | ||
regularization | Range (0.001, 1000) | 60 | ||
KNN | Euclidean | k_neighbors | Range (1, 30, step = 1) | 4 |
Manhattan | 4 | |||
Chebyshev | 24 | |||
LR | Ridge | alpha | Range (0.0001, 1000) | 2 |
Lasso | 1 | |||
Elastic | 0.6 | |||
RF | n_estimators | Range (10, 100, step = 10), Range (100, 200, step = 20), and Range (250, 500, step = 50) | 30 | |
max_depth | Range (1, 15, step = 1) | 6 | ||
min_samples_split | 2, 3, 4, 5, 6, 7, 8, 9, 10 | 7 | ||
max_features | 1, 2, 3, 4, 5, 6 | 6 | ||
SVM | Linear | Cost | Range (0.1, 500) | 5 |
eplison | Range (0.1, 500) | 100 | ||
iteration | Range (10, 500, step = 10) | 50 | ||
Polynomial | Cost | Range (0.1, 500) | 500 | |
eplison | Range (0.1, 500) | 30 | ||
iteration | Range (10, 500, step = 10) | 100 | ||
Rbf | Cost | Range (0.1, 500) | 500 | |
eplison | Range (0.1, 500) | 0.1 | ||
iteration | Range (10, 500, step = 10) | 90 | ||
Sigmoid | Cost | Range (0.1, 500) | 50 | |
eplison | Range (0.1, 500) | 0.5 | ||
iteration | Range (10, 500, step = 10) | 80 |
ML Models | RPD Value | Performance |
---|---|---|
ANN-ReLu | 3.16 | Excellent |
SVM-Polynoimal | 3.00 | Excellent |
ANN-Logistic | 2.92 | Excellent |
RF | 2.72 | Excellent |
ANN-tanh | 2.71 | Excellent |
SVM-RBF | 2.56 | Excellent |
Ridge regression (L2) | 2.42 | Good |
Elastic net regression | 2.41 | Good |
SVM-Linear | 2.40 | Good |
Lasso regression (L1) | 2.39 | Good |
ANN-Identity | 2.38 | Good |
Linear regression | 2.36 | Good |
KNN-Manhattan | 2.11 | Good |
KNN-Euclidean | 1.77 | Fair |
KNN-Chebyshev | 1.65 | Fair |
SVM-Sigmoid | 1.61 | Fair |
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Cha, G.-W.; Choi, S.-H.; Hong, W.-H.; Park, C.-W. Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas. Int. J. Environ. Res. Public Health 2023, 20, 107. https://doi.org/10.3390/ijerph20010107
Cha G-W, Choi S-H, Hong W-H, Park C-W. Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas. International Journal of Environmental Research and Public Health. 2023; 20(1):107. https://doi.org/10.3390/ijerph20010107
Chicago/Turabian StyleCha, Gi-Wook, Se-Hyu Choi, Won-Hwa Hong, and Choon-Wook Park. 2023. "Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas" International Journal of Environmental Research and Public Health 20, no. 1: 107. https://doi.org/10.3390/ijerph20010107