Automating Dataset Generation for Object Detection in the Construction Industry with AI and Robotic Process Automation (RPA)

Buildings, Journal Year: 2025, Volume and Issue: 15(3), P. 410 - 410

Published: Jan. 28, 2025

The construction industry is increasingly adopting artificial intelligence (AI) to enhance productivity and safety, with object detection in visual data serving as a vital tool. However, developing robust models demands extensive, high-quality datasets, which are often difficult generate maintain due the dynamic complex nature of job sites. This paper presents an innovative approach automating dataset generation using robotic process automation (RPA) generative AI techniques, specifically, DALL-E 2. not only accelerates creation but also improves model performance by delivering balanced, inputs. To validate proposed methodology, case study building site conducted. In this study, three commonly used convolutional neural network architectures—RetinaNet, Faster R-CNN, YOLOv5—are trained artificially generated automate identification formworks rebars during construction.

Language: Английский

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Applications and Trends of Machine Learning in Building Energy Optimization: A Bibliometric Analysis

Buildings, Journal Year: 2025, Volume and Issue: 15(7), P. 994 - 994

Published: March 21, 2025

With the rapid advancement of machine learning (ML) technologies, their innovative applications in enhancing building energy efficiency are increasingly prominent. Utilizing tools such as VOSviewer and Bibliometrix, this study systematically reviews body related literature, focusing on key emerging trends cutting-edge ML techniques, including deep learning, reinforcement unsupervised optimizing performance managing carbon emissions. First, paper delves into role prediction, intelligent management, sustainable design, with particular emphasis how smart systems leverage real-time data analysis prediction to optimize usage significantly reduce emissions dynamically. Second, summarizes technological evolution future sector identifies critical challenges faced by field. The findings provide a technology-driven perspective for advancing sustainability construction industry offer valuable insights research directions.

Language: Английский

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Optimized Controller Design Using Hybrid Real-Time Model Identification with LSTM-Based Adaptive Control

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 2138 - 2138

Published: Feb. 18, 2025

Most of the processes with various dynamic characteristics can be reduced to Second Order Plus Time Delay (SOPTD) model by using reduction method. We propose a novel hybrid approach that combines Long Short-Term Memory (LSTM)-based real-time identification Genetic Algorithms enhance Smith predictor control structure. This method compensates for delay time SOPTD while minimizing Integral Absolute Error performance index. Our integrates an optimally adaptive Proportional–Integral–Derivative (PID) controller design algorithm estimates coefficients in Predictor structure and adjusts PID parameters dynamically. The is improved through combination numerical calculation, Algorithms, LSTM networks, showing approximately 15% better compared conventional methods. system demonstrates significant improvements both metrics resource utilization, including 40% execution enhanced efficiency. Simulation results show proposed scheme exhibits adaptability disturbances process variations, faster response times overshoots traditional steady-state higher-order shows perfect matching unit feedback input.

Language: Английский

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Impact of Industry 5.0 on the Construction Industry (Construction 5.0): Systematic Literature Review and Bibliometric Analysis

Buildings, Journal Year: 2025, Volume and Issue: 15(9), P. 1491 - 1491

Published: April 28, 2025

The construction industry is undergoing a paradigm shift with the advent of Construction 5.0 (C5.0), which integrates artificial intelligence (AI), Internet Things (IoT), digital twins, blockchain, and robotics to enhance productivity, sustainability, resilience. This study conducts systematic literature review bibliometric analysis 78 scholarly sources published between 2022 2025, using data from Scopus following PRISMA method. Keyword co-occurrence mapping, citation analysis, content are utilized identify key advancements, emerging trends, adoption challenges in C5.0. Seven core technologies examined through lenses human–robot collaboration (HRC), resilience, revealing rapidly expanding yet still nascent research domain. While C5.0 presents transformative potential, its widespread implementation faces significant barriers. A critical evaluation these conducted, alongside strategic pathways facilitate maximize impact. Furthermore, leading countries seminal contributions field highlighted guide future efforts. By addressing knowledge gaps this provides practical insights for policymakers, researchers, professionals, contributing development innovative frameworks that efficiency, resilience era Industry 5.0.

Language: Английский

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