# Experimental machine learning approach for building structural health monitoring application

Mohamed Oualid Mghazli, Zineb Zoubir, Antoine Rallu, Nouzha Lamdouar, Mohamed Elmankibi

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## To cite this version:

Mohamed Oualid Mghazli, Zineb Zoubir, Antoine Rallu, Nouzha Lamdouar, Mohamed Elmankibi. Experimental machine learning approach for building structural health monitoring application. Journal of Physics: Conference Series, 2024, 2647 (18), pp.182031. 10.1088/1742-6596/2647/18/182031 hal-04922428v2

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Submitted on 12 May 2025

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# Experimental machine learning approach for building structural health monitoring application

To cite this article: Mohamed Oualid Mghazli et al 2024 *J. Phys.: Conf. Ser.* **2647** 182031

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# Experimental machine learning approach for building structural health monitoring application

Mohamed Oualid MGHAZLI<fnref n="1" />,<fnref n="2" />,<fnref n="3" />,*, Zineb ZOUBIR<fnref n="1" />, Antoine Rallu<fnref n="2" />, Nouzha LAMDOUAR<fnref n="2" />, Mohamed ELMANKIBI<fnref n="1" />

## Abstract:

The research and development field of structural health monitoring is evolving, and as a result, it now provides choices for disaster prevention and life cycle extension. This can help boost structural safety and infrastructures resiliency. Researchers have taken an interest in the use of machine learning techniques for these applications as a result of the promising outcomes that those approaches have delivered. In spite of this, damage detection applications necessitate the use of sophisticated and reliable data-driven enabled technologies. This is because of the complex behavior of structures that is notoriously difficult to track. In this investigation, triaxial accelerometers are used for data acquisition purposes in order to monitor and examine the structural performance of an experimental four-story building frame when subjected to a variety of structural configurations. The effectiveness of supervised machine learning algorithms was analyzed and compared to that of unsupervised learning approaches. In spite of the fact that supervised models have demonstrated good performances for the detection of initially established anomalies, they have only partially exhibited performances for newly produced damage patterns, which restricts their application in real life cases. The results obtained by the unsupervised algorithms, on the other hand, were comparable and satisfying, revealing essential capabilities for damage detection applications.

## 1. Introduction

The operational conditions cause civil structures to lose their integrity over time, leaving them susceptible to fatigue, overload, and extreme hazards like wind and earthquakes. To reduce both economic and human losses many inspection procedures and standards, including visual inspection and nondestructive evaluation, have been recommended[1]. Yet, they can be risky, expensive, and time-consuming. In recent decades, Structural Health Monitoring (SHM) has attracted a lot of attention from scientists because it offers a useful method of evaluating and predicting structural performance in real-world operating conditions. It is the process of monitoring the essential properties of a structure in order to detect and analyze early warning indicators of malfunction, abnormality, or deterioration that could jeopardize its usefulness or safety in operation[2].

Due to machine learning advances and the challenges of modeling complex dynamical systems, damage assessment in civil and mechanical systems [3] has taken an interesting turn. Structural health monitoring (SHM) increasingly leverages robust and efficient data-driven methods to infer Damage Sensitive Features (DSF) from structure dynamics[4].

Structural health monitoring uses two methods: Model-driven[5] technique uses a finite element model updated to reflect the structural behavior of the real structure as a baseline. Continuous updating allows comparison of structural changes. Unfortunately, an accurate finite element model is

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<footnote n="1">1 Green Energy Park (IRESEN,UM6P) km2 R206 benguerir</footnote>

<footnote n="2">2 ENTPE, LTDS UMR CNRS 5513, Univ Lyon, Vaulx-en-Velin Cedex, France</footnote>

<footnote n="3">3 Civil Engineering Laboratory, Mohammadia School of Engineers, Mohammed V University in Rabat, Morocco</footnote>

<footnote>*Corresponding Author: mghazli@hotmail.com</footnote>
necessary, which limit the application of such method in real life. The second technique is data-driven [6] approaches which use statistical pattern recognition to take advantage of machine learning's efficiency. Supervised algorithms can be used when measurements in undamaged and damaged states are available, but in real life applications, sensing systems frequently only collect unlabeled data. Therefore, we use unsupervised algorithms which evaluate and detect unlabeled data clusters and patterns.

new research studies have mostly examined damage detection algorithms and focused on their performance. fathnejat et al [7] suggested a one-dimensional convolutional neural network (1DCNN) and recurrent neural network (RNN) employing acceleration time history as a raw input data, with an emphasis on algorithm accuracy and processing costs as compared to other deep learning architectures. Understanding how hyper parameters and design network choices affect deep learning algorithms' performance was made clear by Zou et al[8]. Shuai Ten et al [9]presented a supervised framework for the structural damage identification as he applied approaches based on the convolutional neural network (CNN). The latter have been shown to be effective on simulated buildings. However, their general deployment on real buildings is limited since they require a significant number of training samples. An adaption domain technique for damage detection was proposed by Zhou et al. [10], where an aluminum alloy stiffened panel was used as a case study for the algorithm. In their study, Hoang-le Minh and colleagues [11] developed a combined finite element model update and improved cuckoo search algorithms for damage identification and localisation.

## 2. Methodology description

The four-story steel structure that was built by University of British Columbia [12] was considered for our experimental investigation as a benchmark like several studies before[13–16][7]. For a total of fifteen accelerometers, three acceleration sensors were mounted on each floor. [12] describes the prototypes healthy state and emphasize the eight damage cases that was considered in this study as summarized in Table 1.

<table><caption>Table 1: Health states of the benchmark structure</caption><thead><tr><td>N</td><td>Health State</td></tr></thead><tbody><tr><td>0</td><td>One structure side is brace-free.</td></tr><tr><td>1</td><td>Braces on all floors in one bay are removed</td></tr><tr><td>2</td><td>One bay removes braces on the 1st and 4th floors.</td></tr><tr><td>3</td><td>1st bay braces are removed.</td></tr><tr><td>4</td><td>Braces on all floors on the east face are removed and braces on the 2nd floor on the north face are removed</td></tr><tr><td>5</td><td>All faces are brace-free.</td></tr><tr><td>6</td><td>All braces on all faces are removed and bolts of beams on east face on all floors are loosened</td></tr><tr><td>7</td><td>Braces are removed from all faces and beam bolts on the east face on the 1st and 2nd levels are loosened.</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>Figure 1 Sensor A02's Acceleration (m/s²) in healthy state</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 2 Sensor A02's Acceleration (m/s²) in damaged state</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 3 Distribution of Sensor's DA02 acceleration in healthy and damaged states</figcaption></figure>

In order to assess the structural operational performance of our benchmark structure, we use the Decision Tree and the Random Forest supervised classifiers on a labeled vibration data. The results will then be compared to the performance of AutoEncoder classifier as a widely used unsupervised model for anomaly detection. The goal will be to assess the reliability and the performance of unsupervised models in structural health monitoring applications as labeled unhealthy data are difficult to acquire.

### 3. Supervised Learning

Supervised learning is a class of machine learning algorithm in which the computer is trained on a labeled dataset to make predictions or classifications on new, unlabeled data. The labeled dataset consists of input features and corresponding output values, which are used to train the model to make accurate predictions on new input data. The model learns from the labeled dataset by adjusting its internal parameters until it can predict the correct output values for new input data. The goal of supervised learning is to minimize the difference between the predicted output values and the true output values in the training data, allowing the model to generalize to new, unseen data.

In our case study, we use the Random Forest and Decision Tree algorithms which are all popular algorithms in supervised machine learning for classification tasks.

A Decision Tree is a tree-like structure where each node represents a feature, each branch represents a decision, and each leaf node represents a class label. Decision Trees are easy to interpret and visualize, but they can suffer from overfitting.

Random Forest is an ensemble learning method that constructs multiple Decision Trees and aggregates their predictions to improve accuracy and reduce overfitting. Random Forest is robust and can handle high-dimensional datasets but can be computationally expensive.
The confusion matrices and the ROC curves of the supervised classifiers are shown to demonstrate
the performance of the classification models.

A confusion matrix is a table that summarizes the performance of a classification algorithm on a set of test data. It shows the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) predicted by the algorithm. The confusion matrix is a useful tool for evaluating the performance of a classifier, as it allows you to calculate various metrics such as accuracy, precision, recall, and F1 score.

The ROC (Receiver Operating Characteristic) curve is a plot of the true positive rate (TPR) against the false positive rate (FPR) for different threshold values of a classification model. It helps us to evaluate the performance of a classification model at various threshold settings. A higher area under the ROC curve (AUC) indicates better performance of the model.

<figure><img src="image_10.png" /><figcaption>Figure 4: ROC curves for Decision Tree Classifier</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure 5: Confusion matrix for Decision Tree Classifier</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Figure 6: ROC curves for Random Forest Classifier</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Figure 7: Confusion matrix for Random Forest Classifier</figcaption></figure>
<table><caption>Table 2: Decision Tree model classification results</caption><thead><tr><th>Class Label</th><th>Precision</th><th>Recall</th><th>F1-Score</th></tr></thead><tbody><tr><td>0</td><td>0.91</td><td>0.90</td><td>0.90</td></tr><tr><td>1</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>2</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>3</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>4</td><td>0.96</td><td>0.96</td><td>0.96</td></tr><tr><td>5</td><td>0.98</td><td>0.98</td><td>0.98</td></tr><tr><td>6</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>7</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>8</td><td>1.00</td><td>1.00</td><td>1.00</td></tr></tbody></table>

<table><caption>Table 3: Random Forest model classification results</caption><thead><tr><th>Class Label</th><th>Precision</th><th>Recall</th><th>F1-Score</th></tr></thead><tbody><tr><td>0</td><td>0.96</td><td>0.91</td><td>0.94</td></tr><tr><td>1</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>2</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>3</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>4</td><td>0.96</td><td>0.99</td><td>0.97</td></tr><tr><td>5</td><td>0.98</td><td>0.99</td><td>0.99</td></tr><tr><td>6</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>7</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>8</td><td>1.00</td><td>1.00</td><td>1.00</td></tr></tbody></table>

## 4. Unsupervised Learning

Unsupervised learning is a type of machine learning algorithm in which the computer is trained on an unlabeled dataset to find patterns or structure in the data without any specific guidance. Unlike supervised learning, there are no labeled output values to predict or classify. Instead, the model learns to identify underlying relationships and clusters within the input data by extracting features and grouping similar examples. Unsupervised learning is used in tasks such as anomaly detection, clustering, and dimensionality reduction. The goal of unsupervised learning is to find useful patterns and relationships in the data that can be used for further analysis or decision making, without any prior knowledge or labeling of the data.

In this study, we use the autoencoders as our unsupervised model for anomaly detection. It consists of an encoder that compresses the input into a lower-dimensional latent representation, and a decoder that reconstructs the input from the latent representation. The goal of training an autoencoder is to minimize the reconstruction error between the input and output data.

The reconstruction error that we use in our case is the Mean square Error (MSE) that can be formulated as:

$$MSE = \frac{1}{n} \sum_{i=1}^{n} (X - \hat{X})^2 \quad (1)$$

X: Vector of input acceleration data

$\hat{X}$: Vector of reconstructed acceleration data

As shown in figure 8, the threshold for anomaly was determined by taking the maximum value of the MSE that was calculated in both the training and validation subgroups.

<figure><img src="image_14.png" /><figcaption>Figure 8: Values of MSE for each sample in the training and the validation subset</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure 9: The proposed Autoencoder Loss function</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Figure 10: MSE distribution for each health state</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Figure 11: Comparison of a healthy acceleration sequence and its reconstruction based on the proposed Autoencoder</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Figure 12: Comparison of an unhealthy acceleration sequence and its reconstruction based on the proposed Autoencoder</figcaption></figure>

<table><caption>Table 4: AutoEncoder model classification results</caption><thead><tr><td>Class Label</td><td>Precision</td><td>Recall</td><td>F1-Score</td><td>AUC</td></tr></thead><tbody><tr><td>1</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>2</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>3</td><td>1.00</td><td>0.97</td><td>0.98</td><td>0.98</td></tr><tr><td>4</td><td>0.83</td><td>0.00</td><td>0.00</td><td>0.50</td></tr><tr><td>5</td><td>0.99</td><td>0.00</td><td>0.00</td><td>0.50</td></tr><tr><td>6</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>7</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>8</td><td>1.00</td><td>1.00</td><td>1.00</td><td>1.00</td></tr></tbody></table>

## 5. Discussion

Based on the results of the supervised models, the machine learning algorithms have demonstrated different performances. According to the ROC curves and the confusion matrix in figure 4,5,6 and 7, we can calculate various metrics such as accuracy, precision, recall, and F1 score based on which we can compare the Random forest algorithm with the Decision Trees. Table 2 and table 3 summarizes the results.

the random forest model outperforms the decision tree model, as indicated by the higher area under the ROC curve and lower number of false positives and false negatives in the confusion matrix. One
common result is that their both performances decrease when classifying damage 4 and damage 5 compared to other patterns as they present less accuracy.

Since in real life applications, damaged conditions are usually unavailable, the unsupervised models tend to be more applicable in structural health monitoring purposes. Based on table 4, the autoencoders model that was considered in this study shows comparable and satisfying performance when it comes to detecting operational states (State 1 to 8) except for damage state 4 and 5 where the unsupervised approach has shown some limitations as their AUC score is 0,5 while the other damage pattern's AUC is at least 0,97. This limitation is shown in Figure 10 where we can clearly notice that the MSE distribution of damage 4 and 5 fall below the threshold 0,05 which makes them difficult to detect. The autoencoders have the advantage of being able to capture complex nonlinear relationships in the input data, which is the case for structural dynamics, and extract meaningful features automatically, however, they require large amount of data to achieve good results.

## 6. Conclusion

Existing infrastructure needs structural health monitoring (SHM) frameworks that are reliable, consistent and that can find both local and global structural damage. Machine learning techniques have proven their ability to classify the health state of structures, but their performance has always been dependent on the dataset and the nature of the structure. These algorithms' main goal is a damage detection system with low rates of false positives and false negatives for economic, reliability and life safety issues. Even though damaged/undamaged data is usually not available, it is not possible to use supervised learning models to find damage in real-world structures. In this study, we used a supervised learning framework as a basis of comparison to test the reliability and performance of unsupervised learning algorithms for damage detection. These algorithms tend to be more useful in real-world structures. This paper showed that unsupervised machine learning is almost as good as supervised machine learning at finding important structural damage in building structures.

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