Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
2. Background of the Study
2.1. Rapid Visual Screening
2.2. Machine Learning in Seismic Vulnerability Assessment
3. Data and Methodology
3.1. Input Data Source
3.2. Data Preprocessing
3.2.1. Data Preparation
- Outlier detection—An outlier is a data point that is unlike the other data points. They are rare, discrete, or do not belong in some way. There is no definite technique to distinguish and recognize outliers as usual because of the specifics of each dataset. However, a domain expert can interpret the raw observations and verify if any given data are outliers or not. Identifying outliers can be tricky even after a thorough comprehension of the data. Proper attention should be taken not to eliminate or replace values rashly, specifically when the sample size is small .The input dataset for the study is medium size, so detecting the outliers on the basis of extreme value analysis was possible. There were few data points which were not in the range and distribution of attribute values. Those data points were eliminated to avoid creating any unforeseen circumstance to control the predicting model inaccurately.
- Missing data elimination—Major ML algorithms utilize numeric-type input data values arranged in rows and columns in a given dataset. Missing values in a dataset can create problems for the algorithms to function optimally. Therefore, it is a common practice to identify the missing values and substitute them with a corresponding numerical value. The method is known as missing data imputation or data imputing.A known approach for data imputation is replacing all missing values for that column with the respective column’s mean or median value. - library provides a with a mean or median strategy for missing value elimination. The input dataset for this study is moderately tiny with only 526 values; however, there were 3 NaN (Not a Number) data points raising errors while processing the algorithms. Therefore, using the conventional strategy, those NaN were replaced by the mean value of their respective columns.
- Gaussian data—ML models function better when the data have Gaussian distribution. The Gaussian is a standard distribution with the familiar bell shape. Data fitting techniques can modify each variable to make the distribution Gaussian, or if not Gaussian, then more Gaussian-like. These transforms are most efficient when the data population is nearly Gaussian, to begin with, and is skewed or affected by outliers. Figure 2 shows the histogram for each feature input variable. Floor number has Gaussian-like distribution of the data points, whereas most of the features are skewed toward the left. The captive column obtained a binomial value (0 or 1); therefore, the data distribution is discrete.Figure 3 illustrates the data distribution for each feature input after employing Power-Transformer class from - library. Total floor area and column area show a better Gaussian bell shape, whereas masonry wall area NS and masonry wall area EW have some skewness on the left side. Due to very few non-zero data points, the area of concrete wall area NS and concrete wall area EW did not show any significant improvement.
- Imbalanced Data—An imbalance in data distribution for each input feature creates objections for predictive modeling. In real-world cases, the dataset contains irregular data sharing several times, affecting the model prediction’s performance. Figure 4 depicts that the input dataset contains an asymmetric distribution of feature data which resulted in imbalanced data in each output class. For example, damage class 4 has the highest number of samples, whereas damage class 1 has the least. This kind of data distribution prevents the model from performing optimally. Synthetic Minority Oversampling Technique (SMOTE)  generates synthetic data for the minority class, therefore producing symmetry for majority of classes. Table 3 illustrates that, using SMOTE, the imbalanced state of data distribution in the target variable is balanced.
- Dataset splitting—The suitable approach for performing data preparation with a train–test split evaluation is to fit the data preparation on the training set, then apply the transform to the train and test sets. Therefore, the input dataset was split into (80–20%) into the training set and testing set using function -- from - module in -.
- Cross-Validation—As a good practice, ML models should evaluate the dataset using k-fold cross-validation, particularly in small to medium-sized datasets. Cross-validation aims to examine the model’s worth to predict unseen data, check issues such as model overfitting, or biasedness to give an insight into the model behavior against an independent dataset. The study implemented repeated stratified 10-fold cross-validation using the function RepeatedStratifiedKFold from module ensemble in -.
3.2.2. Feature Selection
3.2.3. Data Transformation
- Standardization—Standardization of a dataset includes re-scaling the spread of values such that the mean of observed values is 0 and the standard deviation is 1. The method is offered by a function called available in the module of - library. Deducing the mean value from the given data is known as centering, and dividing the data by the standard deviation is known as scaling. The method is also referred to as center-scaling.
- Normalization—Data points in any dataset may scale differently from variable to variable. Often ML predictive models perform better if the variables are scaled in a standard range, for example, in the range between 0 and 1. The scaling of all variables in the range between 0 and 1 is known as Normalization. Class from module in - library normalizes the input variable.
- Label Encoder—ML predictive models assume all the provided input and output variables to be numeric. Numerical data include data points that comprise numbers, such as integer or floating-point values. Categorical data involve label values instead of numerical. Categorical variables are frequently known as nominal. - library also has this requirement which implies that all categorical data must be transformed to numerical values.For the dataset applied in this study, the target variable is in the categorical form: the different damage classes assigned to the earthquake-affected RC buildings. With the one-hot encoding method, the categorical output variable can be changed to an ordinal numerical form. The ordinal encoding transform is available in the - library via the class.
3.3. Predictive Model Building
3.3.1. Support Vector Machine
3.3.2. Decision Tree Classifier
3.3.3. Naive Bayes
3.3.4. K-Nearest Neighbor
3.3.5. Random Forest
3.3.6. Gradient Boost
3.4. Selection of ML Classifier
3.5. Web-Development Using ML Model
3.5.1. Design Approach
3.5.2. Project Structure
- Home Page: This application provides the basic home view of the project. The user can see the blank input fields to fill the values of required attributes.
- Predictor: The Submit button will have the functionality to use the ML model and to generate predictions.
- Database (DB): The database or DB button on the home page will connect to another page where all the user-inserted inputs with their predicted values will be saved. In case of no data, the database table will show a blank page.
4. Results and Discussion
- Future studies should consider additional data taking into account the structural system, scale, and damage classifications. In addition, the overall accuracy and robustness of prediction models can be enhanced in future research by adding more extensive datasets (e.g., numerous incidents) and additional site- (e.g., soil conditions) and building-specific predictor variables.
- Concept drift  is an ML phenomenon that focuses on data changes, resulting in the ML model’s testing performance to deteriorate over time. Finally, in the case of RVS, a model’s incorrect forecast might be dangerous as, over time, its performance may decrease. However, this effect can be checked by constantly updating the model and periodically re-fitting the model with new data. Therefore, in the long term, the effect of the concept drift needs to be addressed in any ML-based methodology.
- The web-based application is built in a test-driven development environment. The application is based on the Django framework and uses an internal WSGI gateway and an SQLite3 database. The server and database must be uploaded to the proper server and databases expressly developed for production environments. For heavy-load traffic settings, open-source servers such as Apache and Nginx can be utilized. Other open-source databases, such as MySQL, offer greater security and a broader range of features in a production setting. These ideas will need further research and experimentation, which will be left to future projects.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|ANOVA||Analysis of Variance|
|ANN||Artificial Neural Network|
|CART||Classification and Regression Tree|
|CNN||Convolution Neural Network|
|FEMA||Federal Emergency Management Agency|
|FLDA||Fisher’s Linear Discriminant Analysis|
|GBDT||Gradient Boost Decision Tree|
|MISDR||Maximum Inter-Story Drift Ratio|
|MFPN||Multilayer Feedforward Perceptron Networks|
|MLP-NN||Multilayer Perceptron Neural Network|
|NCREE||National Center for Research on Earthquake Engineering|
|PLS-DA||Partial Least Squares Discriminant|
|RBF||Radial basis function|
|RVS||Rapid Visual Screening|
|SVM||Support Vector Machine|
|SMOTE||Synthetic Minority Oversampling Technique|
|WSGI||Web Server Gateway Interface|
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|No. of Stories||N (1, 2, …)||Integer|
|Total Floor Area||m||Integer|
|Column’s Cross-Sectional Area||m||Integer|
|Concrete Wall Area (Y)||m||Integer|
|Concrete Wall Area (X)||m||Integer|
|Masonry Wall Area (Y)||m||Integer|
|Masonry Wall Area (X)||m||Integer|
|Captive Columns||N (exist = yes = 1, absent = no = 0)||Binomial|
|Damage Scale||Risk Association|
|1||No visible damage to the structure. Safe to reoccupy.|
|2||Low damage. Hairline to wide cracks in the structural elements. Spalling of concrete may also be observed.|
|3||Significant loss. Failure of at least one element in the structure.|
|4||Severe damage. At least one floor slab or part of it loses its elevation.|
|Target Class||Imbalanced Data||Balanced Data|
|ML Classifier||Dataset (before Preprocessing)||Dataset (after Preprocessing)|
|Training Set(in %)||Test Set (in %)||Training Set (in %)||Test Set (in %)|
|Gradient Boost Decision Tree||77||49||78||51|
|Support Vector Machine||57||45||52||47|
|Target Class||Number of Data Predicted||Correctly Predicted Data||Accuracy (in %)|
|Target Class||Number of Data Predicted||Correctly Predicted Data||Accuracy (in %)|
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Kumari, V.; Harirchian, E.; Lahmer, T.; Rasulzade, S. Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings. Buildings 2022, 12, 578. https://doi.org/10.3390/buildings12050578
Kumari V, Harirchian E, Lahmer T, Rasulzade S. Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings. Buildings. 2022; 12(5):578. https://doi.org/10.3390/buildings12050578Chicago/Turabian Style
Kumari, Vandana, Ehsan Harirchian, Tom Lahmer, and Shahla Rasulzade. 2022. "Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings" Buildings 12, no. 5: 578. https://doi.org/10.3390/buildings12050578