Building Surface Defect Detection Using Machine Learning and 3D Scanning Techniques in the Construction Domain

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 669 - 669

Published: March 2, 2024

The rapid growth of the real estate market has led to appearance more and residential areas large apartment buildings that need be managed maintained by a single developer or company. This scientific article details development novel method for inspecting in semi-automated manner, thereby reducing time needed assess requirements maintenance building. paper focuses on an application which purpose detecting imperfections range building sections using combination machine learning techniques 3D scanning methodologies. research design learning-based utilizes Python programming language PyTorch library; it builds team′s previous study, they investigated possibility applying their expertise creating construction-related applications real-life situations. Using Zed camera system, pictures various components were used, along with stock images when needed, train artificial intelligence model could identify surface damage defects such as cracks differentiate between naturally occurring elements shadows stains. One goals is develop can while readily available tools order ensure practical affordable solution. findings this study have potential greatly enhance availability defect detection procedures construction sector, will result better structural integrity.

Language: Английский

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Artificial Intelligence for Routine Heritage Monitoring and Sustainable Planning of the Conservation of Historic Districts: A Case Study on Fujian Earthen Houses (Tulou)

Buildings, Journal Year: 2024, Volume and Issue: 14(7), P. 1915 - 1915

Published: June 22, 2024

With its advancements in relation to computer science, artificial intelligence has great potential for protecting and researching the world heritage Fujian earthen houses (Tulou) historical district. Wood is an important material used construction of (Tulou); wood both main structure buildings decoration. However, professionals must invest significant time energy evaluating any damage before repairing a building. In this context, study proposes optimizes detection method based on YOLOv8 model detecting wooden houses. Through multiple experiments adjustments, we gradually improved performance verified effectiveness reliability practical applications. The results are as follows: (1) This machine-learning-based object can efficiently accurately identify damaged contents, overcoming limitations traditional evaluation methods terms labor costs. approach will aid daily protection monitoring districts serves preliminary their renewal restoration. (2) rounds experiments, optimized significantly accuracy stability by removing samples with complex backgrounds, improving label quality, adjusting hyperparameters. final experiment, model’s overall mAP was only 57.00% at most. during field test, successfully identified nearly all points, including holes, stains, cracks analyzed building, effectively fulfilling requirements task. (3) KuiJu Lou test Tulou, also performed well environments able reliably detect types such structure. confirmed efficiency applications provided reliable technical support Tulou

Language: Английский

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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism

Buildings, Journal Year: 2025, Volume and Issue: 15(2), P. 176 - 176

Published: Jan. 9, 2025

The digital recognition and preservation of historical architectural heritage has become a critical challenge in cultural inheritance sustainable urban development. While deep learning methods show promise classification, existing models often struggle to achieve ideal results due the complexity uniqueness buildings, particularly limited data availability remote areas. Focusing on study Chinese architecture, this research proposes an innovative framework that integrates Swin Transformer backbone with custom-designed Global Channel Spatial Attention (GCSA) mechanism, thereby substantially enhancing model’s capability extract details comprehend global contextual information. Through extensive experiments constructed building dataset, our model achieves outstanding performance over 97.8% key metrics including accuracy, precision, recall, F1 score (harmonic mean precision recall), surpassing traditional CNN (convolutional neural network) architectures contemporary models. To gain deeper insights into decision-making process, we employed comprehensive interpretability t-SNE (t-distributed Stochastic Neighbor Embedding), Grad-CAM (gradient-weighted class activation mapping), multi-layer feature map analysis, revealing systematic extraction process from structural elements material textures. This offers substantial technical support for modeling establishing foundation damage assessment. It contributes formulation precise restoration strategies provides scientific basis governments institutions develop region-specific policies conservation efforts.

Language: Английский

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Exploring Connectivity Dynamics in Historical Districts of Mountain City: A Case Study of Construction and Road Networks in Guiyang, Southwest China

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2376 - 2376

Published: March 8, 2025

As urbanization accelerates globally, preserving and developing historical cultural districts is increasingly critical, especially in areas with unique value. To understand the development of urban construction diachronic spatial patterns development, this paper focuses on Guiyang, a key transportation hub Southwest China connected to Southeast Asia. It examines from four representative periods: early Ming Dynasty (1413–1420), Qing (1616–1626), Republican era (1912–1949), 1980s (1980–1990). Employing complex network analysis, study investigates changes connectivity characteristics land road networks. Key findings reveal: (1) Stability: The networks stability decreased steadily period 1980s, whereas density exhibited wave-like decline. (2) Centrality: centrality linearly, decrease. (3) Vulnerability: Both showed increased vulnerability, fluctuations during period, but generally reduced vulnerability. analysis also indicates that Guiyang’s district are influenced by shifts social structures, improvements productivity, physical geography area. In mountainous cities limited terrain, forms have transitioned single-center aggregation multi-center aggregation, where administrative expansion not feasible adopted compact strategies. application has proven effective studies, revealing reflect multifaceted internal transformations society, politics, economy, military, culture, significantly impacting formation diverse yet unified national identity. Based these findings, offers recommendations for planning globally.

Language: Английский

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Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example

Published: May 3, 2023

In recent years, artificial intelligence technology has widely influenced the field of design, bringing new ideas to efficiently and systematically solve urban renewal design problems. The purpose this study is create a stylized generation for building facade decoration in historic districts, which will aid control district style form. goal use technical advantages conditional generative adversarial network (CGAN) image transfer method independently designing specific by interpreting data historical facades. research paper based on Putian Fujian Province, through an experiment acquisition, processing screening, model training, generation, matching target area. found that: (1) CGAN can better identify generate decorative districts. It realize overall or partial scheme facade; (2) terms adaptability, provide reference reconstruction, renovation, renovation projects. Especially districts with obvious styles, visualization effect better. addition, it also certain significance determination building; (3) This learn internal laws complex form so as clear attribute. be extended other fields heritage protection enhance practitioners' environment improve efficiency ability professional design.

Language: Английский

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Beijing Symbiotic Courtyard Model’s Post Evaluation from the Perspective of Stock Renewal

Sustainability, Journal Year: 2024, Volume and Issue: 16(14), P. 6112 - 6112

Published: July 17, 2024

Stock renewal is one of the important methods urban renewal, which focuses on optimizing and reusing existing spaces. With increasing land pressure in present-day China need for to be reclassified, thinking mode stock updating has become increasingly important. Beijing symbiotic courtyard a representative model that combines characteristics traditional quadrangle dwelling modern architecture, aiming achieve symbiosis history modernity. After several years implementation, effectiveness this matter concern all parties involved. Therefore, paper takes as observation perspective, describing an evaluation model, contains different types residents living particular historical districts. It attempts propose corresponding strategies, provide more comprehensive angle planning method sustainability renewal. In contrast earlier studies, proposed involve specific mathematical statistical called IPA-KANO addition other methods. For wide range problems including district approach leads potentially less errors than caused by manual operation. This comes from fact data are collected through survey questionnaires big data, so technical restriction using some extent ruled out new approach. Moreover, offers potential cannot handled techniques. calculation, although there still defects, local generally satisfied with model. The result suggests it importance reference can widely promoted vitality regeneration.

Language: Английский

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Integrating Artificial Intelligence and the Internet of Things in Cultural Heritage Preservation: A Systematic Review of Risk Management and Environmental Monitoring Strategies

Buildings, Journal Year: 2024, Volume and Issue: 14(12), P. 3979 - 3979

Published: Dec. 15, 2024

Heritage buildings are increasingly vulnerable to environmental challenges like air pollution and climate change. Traditional preservation methods primarily rely on periodic inspections manual interventions struggle address these evolving dynamic threats. This systematic review analyzes how integrating Artificial Intelligence (AI) Internet of Things (IoT) technologies can transform cultural heritage preservation. Using the PRISMA guidelines, 92 articles from SCOPUS were reviewed, highlighting key risk management monitoring methodologies. The study found that while IoT enables real-time quality structural health monitoring, AI enhances data analysis, providing predictive insights. combination facilitates proactive management, ensuring more resilient conservation strategies. Despite growing use technologies, adoption remains uneven, particularly in regions most impacted by identifies significant research gaps proposes an innovative framework leverages Building Information Modeling (H-BIM) Digital Twin (DT) for continuous maintenance through a multi-step process, beginning with digitalization assets using H-BIM, followed creation digital replicas via DT. By advanced offers adaptive sustainable approach preserving heritage, addressing both immediate threats long-term vulnerabilities. underscores need global, technology-driven response safeguard future generations.

Language: Английский

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