Artificial intelligence acceptance and implementation in construction industry: factors, current trends, and challenges DOI
Nitin Liladhar Rane, Saurabh Choudhary, Jayesh Rane

и другие.

SSRN Electronic Journal, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

The construction industry, historically hesitant in adopting new technologies, is undergoing significant transformation with the integration of artificial intelligence (AI). This research paper delves into various elements influencing AI acceptance and implementation within this sector. study applies well-established models theories technology acceptance, including Technology Acceptance Model (TAM), Unified Theory Use (UTAUT), Innovation Diffusion (IDT), specifically adapted to unique context industry. Critical factors driving encompass perceived usefulness, ease use, organizational readiness, top management support, external pressures. Furthermore, highlights essential such as workforce skills, data availability, cybersecurity concerns that considerably affect adoption. Current trends reveal an increasing utilization project management, predictive maintenance, design optimization, a notable surge adoption AI-powered Building Information Modeling (BIM) robotics. Despite these advancements, industry encounters challenges, high costs, resistance change, lack standardization. These challenges are intensified by industry's fragmented nature complexity projects. offers comprehensive review current state providing insights evolving ongoing challenges. It emphasizes need for strategic initiatives foster promoting more efficient, innovative, sustainable environment.

Язык: Английский

The Role of Green Finance in Driving Artificial Intelligence and Renewable Energy for Sustainable Development DOI
Anis Omri, Fadhila Hamza,

Sana Slimani

и другие.

Sustainable Development, Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

ABSTRACT This study contributes to the literature on sustainable development by investigating mechanisms through which green finance fosters sustainability in emerging economies. Given increasing importance of artificial intelligence (AI) and renewable energy environmental transitions, we explore their roles as mediators relationship between sustainability. Using a dataset covering 2015–2022, apply Baron Kenny's (1986) mediation approach combined with advanced econometric techniques assess finance's direct indirect effects development. Our findings reveal that directly enhances while significantly promoting AI capacity. However, once these are included, effect weakens, indicating partial effect. Moreover, identifies additional mediating role linking capacity amplifying its overall impact. These results highlight critical interplay finance, AI, achieving economic Policymakers economies should prioritize initiatives, invest AI‐driven clean solutions, support decentralized projects accelerate transitions.

Язык: Английский

Процитировано

1
How can artificial intelligence adoption enhance manufacturing firms' green management capability? DOI
Na Zhao,

Wenjiang Chen

Finance research letters, Год журнала: 2025, Номер unknown, С. 107475 - 107475

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

1
Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures DOI Creative Commons

Raveena Marasinghe,

Tan Yiğitcanlar, Severine Mayere

и другие.

Journal of Geovisualization and Spatial Analysis, Год журнала: 2024, Номер 8(2)

Опубликована: Июнь 26, 2024

Abstract Artificial intelligence (AI) has increasingly been integrated into various domains, significantly impacting geospatial applications. Machine learning (ML) and computer vision (CV) are critical in urban decision-making. However, AI implementation faces unique challenges. Academic literature on responsible largely focuses general principles, with limited emphasis the domain. This important gap scholarly work could hinder effective integration Our study employs a multi-method approach, including systematic academic review, word frequency analysis insights from grey literature, to examine potential challenges propose strategies for (GeoAI) integration. We identify range of practices relevant complexities using planning its implementation. The review provides comprehensive actionable framework adoption domain, offering roadmap researchers practitioners. It highlights ways optimise benefits while minimising negative consequences, contributing sustainability equity.

Язык: Английский

Процитировано

7
Mapping four decades of housing inequality research: Trends, insights, knowledge gaps, and research directions DOI Creative Commons
Mahla Tayefi Nasrabadi, Taimaz Larimian, Andrew Timmis

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105693 - 105693

Опубликована: Июль 24, 2024

Housing inequality is a pressing issue that affects the lives of millions people worldwide. This study aims to determine trends, generate insights, and identify knowledge gaps in housing research by systematically mapping analysing academic literature. As for systematic literature review method, PRISMA approach employed published during last four decades. The enriched with bibliometric analytics—e.g., trends; influential publications, co-occurrence network terms, geographical distribution—and content analysis techniques provide future directions. revealed main themes, comprising discrimination, market urbanisation, relationship health education, inequalities among young adult population. majority these studies centred their on China. findings following areas consolidate understanding inequality: (a) as product dynamics; (b) condition affecting different segments population disparately; (c) socio-cultural concept; (d) an outcome public policy. advocates multifaceted policy interventions, findings, which contribute achieving relevant Sustainable Development Goals (SDGs), insights urban policymakers planners addressing problems.

Язык: Английский

Процитировано

7
Analysis of green energy-oriented sustainable development goals for emerging economies DOI Creative Commons
Md Shabbir Alam, Hasan Dınçer, Khalid M. Kisswani

и другие.

Journal of Open Innovation Technology Market and Complexity, Год журнала: 2024, Номер 10(3), С. 100368 - 100368

Опубликована: Авг. 24, 2024

Язык: Английский

Процитировано

7
A systematic review of current AI techniques used in the context of the SDGs DOI Creative Commons
Lucas Greif,

Fabian Röckel,

Andreas Kimmig

и другие.

International Journal of Environmental Research, Год журнала: 2024, Номер 19(1)

Опубликована: Окт. 24, 2024

Abstract This study aims to explore the application of artificial intelligence (AI) in resolution sustainability challenges, with a specific focus on environmental studies. Given rapidly evolving nature this field, there is an urgent need for more frequent and dynamic reviews keep pace innovative applications AI. Through systematic analysis 191 research articles, we classified AI techniques applied field sustainability. Our review found that 65% studies supervised learning methods, 18% employed unsupervised learning, 17% utilized reinforcement approaches. The highlights neural networks (ANN), are most commonly contexts, accounting 23% reviewed methods. comprehensive overview identifies key trends proposes new avenues address complex issue achieving Sustainable Development Goals (SDGs). Graphic abstract

Язык: Английский

Процитировано

5
Advanced building envelope by integrating phase change material into a double-pane window at various orientations DOI
Qudama Al-Yasiri, Ahmed K. Alshara,

Murtadha Al Sudani

и другие.

Energy and Buildings, Год журнала: 2024, Номер unknown, С. 115140 - 115140

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

4
Analysis of sustainability assessments to understand modeling trends and habits in the Agri-food industry: A Canadian case study DOI

Joël Mongeon,

Ebenezer Miezah Kwofie, Raphael Aidoo

и другие.

Environmental Impact Assessment Review, Год журнала: 2025, Номер 114, С. 107959 - 107959

Опубликована: Май 3, 2025

Язык: Английский

Процитировано

0
Predicting the number of bidders in construction competitive bidding using explainable machine learning models DOI Creative Commons
Bee Lan Oo, Anh Tuan Nguyen, Yonghan Ahn

и другие.

Construction Innovation, Год журнала: 2025, Номер 25(7), С. 158 - 188

Опубликована: Май 1, 2025

Purpose The number of bidders in upcoming tenders has important managerial implications for both construction clients and contractors their decision-making the competitive bidding process. However, there is a stagnation research efforts on predicting with only handful studies over past decades, which mainly focused statistical distribution bidders. This study aims to provide new perspective using machine learning (ML) algorithms. Design/methodology/approach adopted case approach dataset public sector projects Singapore. Six ML models were developed, linear regression was used as baseline model assessing predictive performance models. Findings results show that outperform model, XGBoost best performing R 2 two times higher than model. In addition, economic-related factors play vital role this prediction problem. Research limitations/implications While developed relatively low, it indicates challenges complexities problem, even use artificial intelligent techniques. Originality/value Being pioneering work, sets foundation problem offers insights future modelling attempts towards development decision support system contractors.

Язык: Английский

Процитировано

0
AI- and GIS-driven product service systems advance sustainable development in dense urban areas DOI
MEHDI ALYARI, Mauricio Novoa Muñoz,

Maral Ahmadi

и другие.

Опубликована: Май 16, 2025

Abstract The United Nations (UN) has taken an active role in using and integrating artificial intelligence (AI) with Geographic Information Systems (GIS) to grow its Sustainable Development Goals (UN SDGs). Among these are Goal 4, Quality Education; 11, Cities Communities; 12, Responsible Consumption Production; 13, Climate Change. This study explores sustainable furniture for the public space (locale that allows democratic opinion, discussion, participation) spaces (physical locations such as streets, parks, squares). AI (Hektar pro) GIS can help assess urban futures simulations prototypes foresee viable solutions challenges climate change, renewable energy resources, social inequality, community involvement. Several typologies of buildings landscapes were presented facilitate constructive communication, reflection, visualization potential among participants. interconnected bridges network is approached open-ended product-service system a futuristic overview interaction, rather than finished object completed at manufacturing time. structure be interpreted various ways regarding use densely populated cities. Energy-wise projections based on PV WATTS simulation software, providing reliable accurate estimation solar production. claims implementing may double perception functional improve generation dense When properly calculated implemented residential areas city scale, save significant amounts user consumption.

Язык: Английский

Процитировано

0