crossrefBuildings2024-04-17Cited by 25

Digital Twin Research on Masonry–Timber Architectural Heritage Pathology Cracks Using 3D Laser Scanning and Deep Learning Model

Due to various factors such as aging, natural environment erosion, and man-made destruction, architectural heritage has formed various diseases and cracks, especially in pathology cracks, which are the most typical masonry–timber architectural heritages, directly affecting the structural stability of masonry–timber buildings. This paper uses artificial intelligence and architecture and other multi-disciplinary research methods, taking James Jackson Gymnasium, a famous masonry–timber architectural heritage in Wuhan, as an example, using 3D laser scanning technology to obtain disease details and crack data of architectural heritage, using a Mask R-CNN model to detect crack area, using an FCN model to identify and calculate single cracks, and finally summarizing the type, location, and characteristics of cracks, analyzing the causes of cracks, and then putting forward corresponding hierarchical restoration strategies. The research results build a set of detection and repair systems of masonry–timber architectural heritage pathology cracks, which provide a set of accurate and objective pathology cracks data for architectural heritage protection and repair, and provide a reference for architectural heritage repair.

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Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models

B. R. Kavya, A. S. Shrikanth, K. S. Sreekeshava

The shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm…

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crossrefBuildings2026-07-15

Temperature-Induced Error Compensation in Computer Vision-Based Displacement Measurement Using Deep Learning-Based Time Series Forecasting Model

Xiaoyan Liu, Cheng Zeng, Feng Li, Yongding Tian

Computer vision technology has emerged as a promising approach for multipoint displacement monitoring of civil infrastructure, owing to its inherent noncontact operation and remote measurement capabilities. However, its measurement accuracy is greatly affected by ambient temperat…

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crossrefBuildings2024-06-20Cited by 1

Analyzing Land Shape Typologies in South Korean Apartment Complexes Using Machine Learning and Deep Learning Techniques

Sung-Bin Yoon, Sung-Eun Hwang

In South Korea, the configuration of land parcels within apartment complexes plays a pivotal role in optimizing land use and facility placement. Given the significant impact of land shape on architectural and urban planning outcomes, its analysis is essential. However, studies on…

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crossrefBuildings2023-06-16Cited by 43

Applicability of Deep Learning Algorithms for Predicting Indoor Temperatures: Towards the Development of Digital Twin HVAC Systems

Pooria Norouzi, Sirine Maalej, Rodrigo Mora

The development of digital twins leads to the pathway toward intelligent buildings. Today, the overwhelming rate of data in buildings carries a high amount of information that can provide an opportunity for a digital representation of the buildings and energy optimization strateg…

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crossrefBuildings2024-01-24Cited by 10

Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections

Gi-Tae Roh, Nhung Vu, Chi-Ho Jeon, Chang-Su Shim

Existing design codes for predicting the strength of stud shear connections in composite structures are limited when adapting to constant changes in materials and configurations. Machine learning (ML) models for predicting shear connection are often constrained by the number of i…

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crossrefBuildings2025-05-08Cited by 12

Digital Twin-Enabled Building Information Modeling–Internet of Things (BIM-IoT) Framework for Optimizing Indoor Thermal Comfort Using Machine Learning

Fahad Iqbal, Shayan Mirzabeigi

As the world moves toward a low-carbon future, a key challenge is improving buildings’ energy performance while maintaining occupant thermal comfort. Emerging digital tools such as the Internet of Things (IoT) and Building Information Modeling (BIM) offer significant potential, e…

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Digital Twin Research on Masonry–Timber Architectural Heritage Pathology Cracks Using 3D Laser Scanning and Deep Learning Model

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Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models

Temperature-Induced Error Compensation in Computer Vision-Based Displacement Measurement Using Deep Learning-Based Time Series Forecasting Model

Analyzing Land Shape Typologies in South Korean Apartment Complexes Using Machine Learning and Deep Learning Techniques

Applicability of Deep Learning Algorithms for Predicting Indoor Temperatures: Towards the Development of Digital Twin HVAC Systems

Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections

Digital Twin-Enabled Building Information Modeling–Internet of Things (BIM-IoT) Framework for Optimizing Indoor Thermal Comfort Using Machine Learning