Artificial Intelligence (AI) and Internet of Things (IoT) - based sensors for monitoring and controlling in architecture, engineering, and construction: applications, challenges, and opportunities

SSRN Electronic Journal, Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

The fusion of Artificial Intelligence (AI) and the Internet Things (IoT) has brought about a paradigm shift in realm architecture, engineering, construction (AEC), introducing intelligent sensing technologies that significantly enhance monitoring control. This study delves into varied applications, hurdles, prospects emerging from collaborative deployment AI IoT-based sensors within AEC domain. AI-equipped smart enable real-time structural health, energy consumption, environmental conditions both buildings infrastructure. These empower predictive maintenance, ensuring durability structures while minimizing downtime. Additionally, AI-driven analytics optimize resource allocation, improve safety protocols, streamline processes, thereby enhancing overall project efficiency. Through ongoing analysis data collected by integrated HVAC systems, elevators, lighting, maintenance teams can pre-emptively tackle potential malfunctions. Furthermore, synergy between IoT enables development with adaptive features. Sensors examine occupancy patterns, lighting preferences, temperature fluctuations play pivotal role crafting energy-efficient occupant-centric building designs. security privacy concerns associated sensor-generated give rise to critical issues necessitate robust cybersecurity measures. Interoperability challenges among diverse sensor networks platforms also present obstacles seamless integration. adoption these demands substantial investments infrastructure workforce training, requiring strategic approach for widespread acceptance. paper explores how capabilities contribute risk mitigation cost reduction across entire lifecycle. Moreover, ability collect analyze vast amounts empowers stakeholders make well-informed decisions, fostering innovation sustainability industry. By addressing underscoring benefits, it provides invaluable insights industry professionals, researchers, policymakers eager harness transformative construction.

Язык: Английский

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Integrating Building Information Modelling (BIM) with ChatGPT, Bard, and similar generative artificial intelligence in the architecture, engineering, and construction industry: applications, a novel framework, challenges, and future scope

SSRN Electronic Journal, Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

The architecture, engineering, and construction (AEC) sector is currently experiencing a profound shift in its paradigm through the incorporation of Building Information Modelling (BIM) advanced generative artificial intelligence (AI) technologies. This study delves into diverse applications this transformative integration, presenting pioneering framework for merging BIM with models like ChatGPT Bard. integrated approach outlined research paper has broad implications. Initially, it explores augmentation design process, where AI enhances creative input architects engineers by generating innovative alternatives rooted data. BIM's collaborative ethos extended natural language interfaces from ChatGPT, fostering seamless communication idea exchange among project stakeholders. In phase, integration streamlines real-time decision-making on-site personnel, providing AI-generated insights based on ensures heightened efficiency, cost-effectiveness, risk management. synergy between also harnessed simulation analysis, enabling predictions related to structural performance, energy environmental impact. introduces an seamlessly AI, prioritizing interoperability, data consistency, user-friendly interfaces. Designed adapt dynamic nature AEC projects, promotes continuous collaboration information exchange. It establishes standardized platform harnessing strengths both technologies, ensuring cohesive efficient workflow across lifecycle. Nevertheless, brings forth challenges, including security, ethical considerations, demand extensive computational resources. provides foundational upcoming studies industry practices, paving way more intelligent, collaborative, ecosystem.

Язык: Английский

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An integrated defect detection method based on context encoder and perception-enhanced aggregation for cylinder bores

Journal of Manufacturing Processes, Год журнала: 2024, Номер 114, С. 196 - 212

Опубликована: Фев. 10, 2024

Язык: Английский

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Automated defects detection in extrusion 3D printing using YOLO models

Journal of Intelligent Manufacturing, Год журнала: 2024, Номер unknown

Опубликована: Дек. 13, 2024

Язык: Английский

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Research on Visual Target Detection and Recognition of Shopping Robots Based on Improved YOLO Algorithm

Lecture notes in electrical engineering, Год журнала: 2025, Номер unknown, С. 323 - 332

Опубликована: Янв. 1, 2025

Язык: Английский

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Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing

Applied Sciences, Год журнала: 2025, Номер 15(7), С. 3577 - 3577

Опубликована: Март 25, 2025

Rotating machinery, as a vital and inevitable component in industrial production processing, plays crucial role ensuring the normal operation of processes. However, most existing fault diagnosis methods for rotating machinery are either offline or cloud-based online approaches, which suffer from long latency large data volumes, making them unable to meet real-time requirements. To reduce transmission volume, this research proposes method based on edge computing. This constructs an node that integrates signal acquisition, preprocessing, feature extraction, classification accurately identify status equipment. address issues low recognition rate redundancy associated with single sensors under complex working conditions, dual-channel CNN decision-level fusion. alleviate computational pressure nodes, equipment model is trained upper computer, preprocessing embedded into nodes. The correctness performance proposed were validated through comparisons other experiments.

Язык: Английский

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Enhancing thermal comfort through leading-edge design, monitoring, and optimization technologies: A review

SSRN Electronic Journal, Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

This research paper offers a thorough examination of strategies geared towards enhancing thermal comfort within constructed spaces through the incorporation state-of-the-art design, monitoring, and optimization technologies. In light escalating challenges brought about by climate change increasing demand for sustainable energy-efficient building solutions, quest improved has emerged as central focus in architectural engineering research. The initiates its exploration delving into contemporary design principles that underscore significance passive strategies, such optimal orientation, shading devices, natural ventilation. It elucidates synergies between elements comfort, emphasizing how innovative designs can foster ideal indoor conditions while reducing reliance on energy-intensive heating, ventilation, air conditioning (HVAC) systems. A substantial segment review is dedicated to monitoring technologies enabling real-time assessment environmental parameters. integration sensors, data analytics, Building Information Modelling (BIM) facilitates nuanced comprehension dynamics, allowing adaptive responses evolving conditions. discusses role wearable devices occupant feedback systems capturing subjective perceptions, thereby enriching pool more comprehensive analysis. Moreover, delves burgeoning field technologies, encompassing utilization machine learning algorithms artificial intelligence management These empower predictive modeling optimizing HVAC operations, minimizing energy consumption. synthesis these not only enhances well-being but also aligns with global endeavours forge resilient built environments amid challenges.

Язык: Английский

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Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in building and construction industry: applications, framework, challenges, and future scope

SSRN Electronic Journal, Год журнала: 2023, Номер unknown

Опубликована: Янв. 1, 2023

The infusion of generative artificial intelligence (AI), as exemplified by models such ChatGPT and Bard is proving to be a revolutionary catalyst within the building construction sector. This exploration delves into myriad applications, establishes conceptual framework, confronts challenges, delineates prospective trajectory harnessing AI across diverse stages lifecycle. In domain project management scheduling, contribute optimal resource allocation, task sequencing, timeline optimization, thereby elevating overall efficiency delivery. Design optimization equally pivotal, assists architects engineers in crafting innovative designs that concurrently adhere functional aesthetic criteria. predictive prowess fortifies risk management, furnishing stakeholders with insights potential risks effective mitigation strategies. Meanwhile, realm cost estimation budgeting, enhanced accuracy speed offered optimize financial planning allocation. Supply chain benefits from streamlined processes driven insights, ensuring timely cost-effective procurement materials. Generative linchpin quality control, identifying defects deviations standards enhance quality. Real-time data analysis strengthens site monitoring safety protocols, enabling proactive secure working environment. Collaboration communication teams are augmented AI, facilitating seamless information exchange decision-making processes. Predictive maintenance asset undergo transformation, algorithms predicting equipment failures optimizing schedules. Furthermore, integration tackles imperative energy sustainability Models like bard significantly for conservation sustainable practices. paper also explores incorporation reality (AR), virtual (VR), Building Information Modeling (BIM). Ethical concerns, privacy, robust cybersecurity measures necessitate careful consideration. As industry embraces these innovations, substantial improvements efficiency, sustainability, outcomes poised unfold.

Язык: Английский

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Exploring End-to-End Object Detection with Transformers versus YOLOv8 for Enhanced Citrus Fruit Detection within Trees

Systems and Soft Computing, Год журнала: 2024, Номер 6, С. 200103 - 200103

Опубликована: Май 21, 2024

This paper presents a comparative analysis between two state-of-the-art object detection models, DETR and YOLOv8, focusing on their effectiveness in fruit for yield prediction agriculture. The study begins with data acquisition, utilizing images corresponding annotations to train evaluate the models. Our approach employs data-driven methodology, dividing dataset into training testing sets, rigorous validation ensure robustness. For DETR, evaluation results demonstrate promising performance across various IoU thresholds, indicating its accurately localizing fruits within bounding boxes. Additionally, YOLOv8 exhibits substantial improvements performance, achieving high precision recall rates, particularly noteworthy "orange" "sweet_orange" classes. Notably, model showcases commendable proficiency even challenging scenarios. In conclusion, both offer valuable insights farming, aiding farmers harvest planning. While demonstrates robustness efficiency detection, excels high-precision albeit longer times. These findings highlight potential of advanced models revolutionizing agricultural practices, contributing enhanced productivity market equilibrium.

Язык: Английский

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Evaluation of digital twin synchronization in robotic assembly using YOLOv8

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 134(1-2), С. 871 - 885

Опубликована: Июль 29, 2024

Язык: Английский

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