Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(8), P. 5014 - 5014

Published: April 17, 2023

In the Industry 5.0 era, companies are leveraging potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. a similar approach, project management would benefit from in order to achieve goals by improving performance, consequently, reaching higher sustainable success. this context, paper examines role emerging through systematic literature review; applications AI techniques performance domains presented. The results show that number influential publications on intelligence-enabled has increased significantly over last decade. findings indicate intelligence, predominantly machine learning, can be considerably useful construction IT projects; it is notably encouraging enhancing planning, measurement, uncertainty providing promising forecasting decision-making capabilities.

Language: Английский

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The Role of Lightweight AI Models in Supporting a Sustainable Transition to Renewable Energy: A Systematic Review

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1192 - 1192

Published: Feb. 28, 2025

The transition from fossil fuels to renewable energy (RE) sources is an essential step in mitigating climate change and ensuring environmental sustainability. However, large-scale deployment of renewables accompanied by new challenges, including the growing demand for rare-earth elements, need recycling end-of-life equipment, rising footprint digital tools—particularly artificial intelligence (AI) models. This systematic review, following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines, explores how lightweight, distilled AI models can alleviate computational burdens while supporting critical applications systems. We examined empirical conceptual studies published between 2010 2024 that address energy, circular economy paradigm, model distillation low-energy techniques. Our findings indicate adopting significantly reduce consumption data processing, enhance grid optimization, support sustainable resource management across lifecycle infrastructures. review concludes highlighting opportunities challenges policymakers, researchers, industry stakeholders aiming integrate principles into RE strategies, emphasizing urgent collaborative solutions incentivized policies encourage low-footprint innovation.

Language: Английский

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Applications of artificial intelligence in the AEC industry: a review and future outlook

Journal of Asian Architecture and Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 17

Published: April 21, 2024

In the context of "Industry 4.0," construction industry is undergoing a continuous process digital transformation. recent years, with rapid development artificial intelligence (AI) technology, AI has been successfully applied across various domains within sector. This paper conducts comprehensive review based on scientific bibliometric analysis and systematic evaluation to provide an overview applications developments in architecture, engineering, (AEC) industry, as well discuss future research trends. Firstly, was carried out 3,410 journal articles published between 2003 2022. examined characteristics such annual publication trends, key researchers their affiliations, countries origin, collaborative relationships, prominent journals, keyword clustering. Subsequently, through review, three critical themes field were identified analyzed: performance prediction, intelligent optimization, engineering safety risk. To gain deeper understanding cutting-edge technologies potential AEC sector, directions discussed, including twins, blockchain, augmented reality, virtual all aimed at driving transformation industry.

Language: Английский

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Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

Buildings, Journal Year: 2023, Volume and Issue: 13(12), P. 2944 - 2944

Published: Nov. 25, 2023

The high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science artificial intelligence (ETs) an alternative in the life cycle. This article aims present systematic review literature on use these architecture, engineering, construction industry. A methodology collection, purification, evaluation, bibliometric, categorical analysis was used. total 224 articles were found, which, using PRISMA method, finally generated 57 articles. focused determining used, most common methodologies, most-discussed areas, contributions AEC found that there is international leadership China, United States, Kingdom. type research used quantitative. areas knowledge where ETs are Cost, Quality, Time, Scope. Finally, among outstanding follows: prediction development projects, identification critical factors, detailed risks, optimization planning, automation tasks, increase efficiency; all facilitate decision making.

Language: Английский

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<b>Optimization of Construction Material Cost through Logistics Planning Using Different Meta-Heuristic Optimization Algorithms: A Comprehensive Study&nbsp;</b>

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Leveraging Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions

Computers, Journal Year: 2025, Volume and Issue: 14(2), P. 66 - 66

Published: Feb. 13, 2025

This article presents a systematic literature review exploring the integration of Artificial Intelligence (AI) methodologies in project management (PM). Key applications include cost estimation, duration forecasting, and risk assessment, which are critical factors for success. synthesizes findings from 97 peer-reviewed studies published between 2011 2024, using PRISMA methodology to ensure rigor transparency. AI techniques such as machine learning, deep hybrid models have exhibited their potential enhance PM across projects’ phases, including planning, execution, monitoring. Decision trees created represent application various stages tasks facilitate understanding real-world implementation. Among these that well categorization based on phases optimize integration. Despite advancements, there still gaps addressing dynamic environments, validating with data, expanding research into underexplored like closure.

Language: Английский

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Smart material optimization using reinforcement learning in multi-dimensional self-assembly

Frontiers in Materials, Journal Year: 2025, Volume and Issue: 12

Published: March 6, 2025

Introduction In recent years the design and optimization of smart materials have gained considerable attention due to their potential applications across diverse fields, from biomedical engineering adaptive structural systems. Traditional approaches for optimizing these often rely on deterministic models ortrial-and-error processes, which tend be limited by computational expense lack adaptability in dynamic environments. These methods generally fail address complexities multi-dimensional self-assembly processes where need respond autonomously environmental stimuli real time. Methods To limitations, this research explores application reinforcement learning (RL) as an advanced framework enhance autonomous materials. We propose a novel learning-based model that integrates control mechanisms within self-assembly, allowing optimize configuration properties according external stimuli. our approach, agents learn optimal assembly policies through iterative interactions with simulated environments, enabling material evolve complex multi-factorial inputs. Results discussion Experimental results demonstrate model’s efficacy, revealing significant improvements adaptability, efficiency, performance under varied conditions. The work not only advances theoretical understanding but also paves way development autonomous, self-optimizing can deployed real-world requiring adaptation robustness.

Language: Английский

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The effect of phase change material application in double skin Façade on energy saving of residential buildings considering different climates: a case study

Energy Sources Part A Recovery Utilization and Environmental Effects, Journal Year: 2023, Volume and Issue: 46(1), P. 209 - 227

Published: Nov. 27, 2023

The amount of energy expenditure in buildings is 30–40% the total consumption world and this share will increase to 50% by 2050. Therefore, reducing a necessity one ways reduce use passive methods.

Language: Английский

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Developing a three stage coordinated approach to enhance efficiency and reliability of virtual power plants

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 7, 2024

Abstract A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed resources, integrating them into unified entity. While the physical assets may be dispersed across various locations, VPP integrates virtual entity capable of responding to grid demands market signals. This paper presents tri-level hierarchical coordinated operational framework VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) introduced optimally schedule Distributed Energy Resources (DERs) within VPP, resulting in significant reduction generation costs. Comparative analysis against conventional algorithms such as Genetic (GA) Particle Swarm (PSO) demonstrates IPOA's superior performance, achieving average 8.5% costs case studies. The second stage focuses on securing optimized data from rising cyber threats, employing capabilities machine learning, preferably, convolutional autoencoder learn normal patterns detect deviations prevent suboptimal decisions. model exhibits exceptional performance detecting manipulated data, with False Positive Rate (FPR) 1.92% Detection Accuracy (DA) 98.06%, outperforming traditional detection techniques. Lastly, delves dynamic nature day ahead participates in. In by selling its generated power via day-ahead market, employs Prophet model, another learning technique forecast spot price for mitigate adverse effects volatility. By utilizing forecasts, achieves revenue increase 15.3% compared scenarios without prediction, emphasizing critical role predictive analytics optimizing economic gains. approach adopted addresses key challenges sector, facilitating progress towards universal access clean affordable energy.

Language: Английский

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Material Distribution Planning Method and Experimental Verification under Multinode and Multivehicle Scene

Journal of Construction Engineering and Management, Journal Year: 2024, Volume and Issue: 150(11)

Published: Aug. 29, 2024

Language: Английский

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