Artificial Intelligence (AI) Applied in Civil Engineering

doi:10.3390/books978-3-0365-5084-8

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…Artificial intelligence (AI) methods have various applications in civil and structural engineering [2,3], in several areas such as: Structural health monitoring [4], structural damage identification [5,6], structural design optimization [7], structural modelling [8][9][10], predictive maintenance [11,12], construction planning and management [13], risk assessment [14,15], predicting strength and other structural characteristics [16][17][18], and energy efficiency [19], among others. AI methods have also been recently used in wall surface defect classification [20].…”

Section: Introductionmentioning

confidence: 99%

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Qayyum,

Ehtisham,

Plevris

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Stakeholders are increasingly interested in finding efficient methods for regularly surveying and reporting on the state of their building assets, focusing on accuracy, consistency, and ease of use. High-rise buildings can exhibit wall surface defects and flaws such as cracks and spalls, which can significantly affect the structures' safety and appearance. Such problems need to be taken care of in a timely manner, before they become too hazardous or costly to fix. In this research work, images of several types of wall damage are classified into three main categories: (i) Undamaged; (ii) Cracked; and (iii) Miscellaneous. In total, 6000 images were used in the dataset, equally subdivided into the three categories. In machine learning, convolutional neural networks (CNNs) stand out as a form of neural network that excels at image classification. A transfer learning approach was implemented to classify wall surface defect images using three pre-trained CNN models, namely ResNet-50, ResNet-101, and Inception V3. 70% of the data set was used for training purposes, and the remaining 30% was used for validation. Several metrics including accuracy, precision, recall, and F1-score were computed for each model, in an attempt to find the best model for the damage classification task at hand. According to the results, Inception V3 demonstrated superior performance compared to the ResNet-50 and ResNet-101 models, achieving an overall accuracy of 87.1%. In contrast, ResNet-101 and Res-Net-50 obtained overall accuracies of 85.3% and 78.3%, respectively. The suggested methodology offers several benefits and a clear potential for broader adoption in the future as it can significantly reduce the time and effort required for manual inspection and classification of defects, allowing for more efficient maintenance and repair processes.

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Section: Introductionmentioning

confidence: 99%

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Qayyum,

Ehtisham,

Plevris

et al. 2023

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…Artificial intelligence (AI) methods have seen several applications in civil and structural engineering [6,7], in many different areas such as: Structural modelling [8-10], structural design optimization [11], predictive maintenance [12,13], SHM [14], risk assessment [15,16], predicting strength [17,18] and other structural characteristics [19], energy efficiency [20], construction planning and management [21], and others. AI methods have also been increasingly utilized in the field of structural engineering for damage detection, offering improved accuracy and efficiency compared to traditional methods [22,23].…”

Section: Introductionmentioning

confidence: 99%

Classification and Computing the Defected Area of Knots in Wooden Structures Using Image Processing and CNN

Ehtisham,

Qayyum,

Plevris

et al. 2023

15th International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control

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Knots in wooden structures are common natural features in wood that result from where branches once joined the trunk of a tree. While they can add to the aesthetic appeal of wood, knots are often considered structural defects in construction because they can significantly affect the mechanical properties of wood. If knots are present in structural members, they cannot be ignored. Identifying the presence of knots and finding the corresponding defected area of a structural member is important to be able to reinforce the member, compensate for the reduced strength and ensure that it is safe and suitable for its intended use. In this study, the Inception-ResNet-V2 pre-trained Convolutional Neural Network (CNN) model is trained and validated with 2000 images for the classification of knots, and the defected area is calculated through Image Processing (IP) and other soft computing techniques. The images of knots are collected and equally classified into two categories: 1000 "Single knot" and 1000 "Multiple knots" images. 70% of the dataset is used for training, and 30% for model validation. Four statistical parameters, namely accuracy, precision, recall, and F1 score, are calculated to check the model performance for the classification task, as well as the corresponding confusion matrix. The model exhibited an overall accuracy of 84% in an independent evaluation with a new testing dataset of 200 images, while the defects could be properly quantified using IP techniques.The research work shows the potential of AI-based methodologies in structural health monitoring and damage identification. These methods can drastically improve our ability to assess the condition of structures and structural elements, offering enhanced precision and accuracy, real-time and cost-effective monitoring, predictive capabilities, and automation opportunities.

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“…Moreover, the data yielded by OpenSeismoMatlab (or any analogous software for strong ground motion processing) could be harnessed through advanced AI techniques, such as artificial neural networks. These methods have the potential to unveil optimal strategies for amalgamating and further processing these output parameters, thereby elucidating pathways for enhanced seismic impact assessment [43,44]. With the advent of AI and with continuously increasing computational power, it is apparent that OpenSeismoMatlab can contribute to the development of more advanced AI tools, or obtain some benefits from such modern technologies [45].…”

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confidence: 99%

OpenSeismoMatlab: New Features, Verification and Charting Future Endeavors

Papazafeiropoulos,

Plevris

2024

Buildings

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To facilitate the precise design of earthquake-resistant structures, it is imperative to accurately evaluate the impact of seismic events on these constructions and predict their responses. OpenSeismoMatlab, a robust, free ground motion data processing software, plays a pivotal role in this endeavor. It empowers users to compute a wide array of outcomes using input acceleration time histories, encompassing time histories themselves, as well as linear and nonlinear spectra. These capabilities are instrumental in supporting structural design initiatives. This study provides a comprehensive exposition of the latest version (v 5.05) of OpenSeismoMatlab. It delves into intricate facets of the software, encompassing a detailed exploration of the input and output variables integral to each operational category. Comprehensive calculation flowcharts are presented to elucidate the software’s organizational structure and operational sequences. Furthermore, a meticulous verification assessment is conducted to validate OpenSeismoMatlab’s performance. This verification entails a rigorous examination of specific cases drawn from existing literature, wherein the software’s outcomes are rigorously compared against corresponding results from prior studies. The examination not only underscores the reliability of OpenSeismoMatlab but also emphasizes its ability to generate outcomes that closely align with findings documented in the established body of literature. Concluding the study, the paper outlines potential directions for future research, shedding light on avenues where further development and exploration can enhance the utility and scope of OpenSeismoMatlab in advancing seismic engineering and structural design practices.

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Artificial Intelligence (AI) Applied in Civil Engineering

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Cited by 12 publications

References 39 publications

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Classification of Wall Defects for Maintenance Purposes Using Image Processing

Classification and Computing the Defected Area of Knots in Wooden Structures Using Image Processing and CNN

OpenSeismoMatlab: New Features, Verification and Charting Future Endeavors

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