Development of a learner model tool for predicting strength and embodied carbon for lightweight concrete production

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 95, P. 110330 - 110330

Published: Aug. 3, 2024

The demand for sustainable concrete in meeting the net zero carbon target places a burden optimizing response to structural strength that satisfy acceptable embodied carbon. In most cases, low is deficient requirement and vice versa. This dilemma informs need tool can predict compressive as well using same input data. Since use of alternative materials cement replacement enhance sustainability emerging quest concrete, an optimal material both conditions integrity still lacking. Paucity data lightweight materials, portends upheave bias prediction behaviour concrete. study therefore uses from laboratory experiment with their performance evaluated eight machine leaning regression models. results obtained indicates XG boost model exhibited excellent Mean Squared Error (MSE) 50.15, absolute error(MAE) = 5.26, percentage error(MAPE) 11.76 %, Explained variance score 0.97, Root mean square error(RMSE) 7.08 high R squared value 0.96. predicted multiple output such be limited yearly threshold achieving 2050 target. developed when compared similar mix ingredients performed more than 95 % predicting associated line inclusion regulations buildings UK suggested by professionals construction industry, learner has integrated initiate holistic approach design construction, balancing performance, cost, environmental impact.

Language: Английский

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Cloud Model-Based Intelligent Construction Management Level Assessment of Prefabricated Building Projects

Buildings, Journal Year: 2024, Volume and Issue: 14(10), P. 3242 - 3242

Published: Oct. 13, 2024

Intelligent construction is vital for achieving new building industrialization by enhancing prefabricated buildings through integrated, digital, and intelligent management across production processes. Despite its significance, detailed research on evaluating the (ICM) level of projects remains limited. This study aims to develop a comprehensive, multi-level, multi-dimensional ICM assessment system. By reviewing literature, engaging in expert discussions, conducting case studies—specifically using project Guangzhou as an example—this employs Analytic Hierarchy Process (AHP) entropy weight methods assign indicator weights. Utilizing cloud model theory, it establishes evaluation standards management. identifies project’s level, suggests practical improvement methods, validates applicability. work not only advances theoretical understanding but also provides framework assessing levels projects, thus contributing significantly field offering perspectives empirical evidence.

Language: Английский

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Review of artificial intelligence applications in construction management over the last five years

Engineering Construction & Architectural Management, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 23, 2024

Purpose This paper provides a thorough examination of the advancements and impacts artificial intelligence (AI) on construction management (CM) over past five years, particularly focusing its role in mitigating prevalent challenges such as inefficiency ensuring quality. By methodically reviewing synthesizing body research conducted this period, it underscores key contributions breakthroughs application AI within (AICM). Additionally, study aims to shed light emerging trends forecast future directions for technological innovation sector. Design/methodology/approach Guided by preferred reporting items systematic reviews meta-analyses (PRISMA) framework, conducts bibliometric analysis 176 relevant publications from years. The focuses adoption AICM across three critical areas: equipment management, improvement safety cost optimization. systematically identifies examines 14 themes domain, comprehensive exploration aligned with PRISMA guidelines. Findings manuscript summarizes recent years realm AICM. It gaps outlines directions, including enhancing AI-driven integration, developing sophisticated AI-based systems optimizing advanced data analytics. These findings are essential steering field toward greater digital sustainability. Originality/value detailed literature thoughtfully compiling significant highlighting importance addressing user needs. insights recommendations shared aim be beneficial both academic researchers industry professionals, contributing ongoing development moves characterized

Language: Английский

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Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons

Studies in computational intelligence, Journal Year: 2022, Volume and Issue: unknown, P. 273 - 319

Published: Jan. 1, 2022

Language: Английский

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Multi objective optimization of detailed building models with Typical Short Sequences considering sequential and adaptive methods

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 118, P. 105645 - 105645

Published: Nov. 30, 2022

Language: Английский

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Applied AI and Robotics for Construction Operations—A Smart Review of the State of the Science

Construction Research Congress 2022, Journal Year: 2024, Volume and Issue: unknown, P. 913 - 923

Published: March 18, 2024

The rapid advancements in Artificial Intelligence (AI), driven by increased computational power, have revolutionized the engineering industry recent years. A growing body of literature has emerged, focusing on application AI, advanced robotics, and Internet Things (IoT) various sectors. However, construction appears to lag adopting these intelligent automation technologies for enhancing operations. This study aims examine current state AI robotics adoption within purposes. To achieve this, a systematic review academic peer-reviewed articles robotic applications was conducted. Keyword-based semi-supervised machine learning employed classify according phases construction. Subsequently, an unsupervised algorithm utilized perform content analysis concerning different phases. enabled better understanding relevant applications, which could be integrated across enhance efficiency. Additionally, this investigates barriers benefits aimed at improving productivity safety. Finally, article discusses implications job opportunities sector.

Language: Английский

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Application of Soft Computing to Address Uncertainty in Construction Project Management: A Systematic Literature Review

Civil Engineering Journal, Journal Year: 2024, Volume and Issue: 10(6), P. 2040 - 2065

Published: June 1, 2024

Decision-making in Construction Project Management (CPM) involves numerous ambiguous information and uncertainties due to the nature of construction project. The Soft Computing (SC) approach, which offers several data processing strategies under uncertainty, has been extensively researched CPM studies for decision problem solving. Decisions that cannot be adequately handled by conventional computer systems are facilitated SC approach. approach encompasses a variety techniques constantly developing becoming more widely used address real challenges. This study aims conduct Systematic Literature Reviews (SLR) on development mainstream their current application projects. Using an inventive SLR technique, 83 papers covering years 2018 2023 were selected this then classified into four primary themes CPM. research trend was described using bibliometric analysis. Afterwards, topic-based qualitative analysis conducted investigate approaches field. Several potential challenges elaborated. It also contributed suggesting future directions advancement would advantageous practice. Doi: 10.28991/CEJ-2024-010-06-020 Full Text: PDF

Language: Английский

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An Intelligent Hybrid Algorithm for Economic Development Difference Analysis Based on Data Analysis

Published: Aug. 9, 2024

Language: Английский

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Inteligencia artificial en la gestión de proyectos: caso construcción y obra civil

European Public & Social Innovation Review, Journal Year: 2024, Volume and Issue: 9, P. 1 - 21

Published: Oct. 16, 2024

Introducción: El presente documento relaciona una investigación con el objetivo establecer los niveles de apropiación tecnologías emergentes, principalmente inteligencia artificial, en la gestión proyectos del sector construcción. Metodología: Se llevó a cabo cuantitativa centrada revisión literatura internacional y determinación nivel madurez tecnológica Colombia. contó participación 97 empresas. Resultados: Los resultados muestran alto interés productivo comunidad académica uso artificial proyectos, priorizando áreas como costos, calidad, tiempos, alcance riesgos. La incorporación software IA, LLM (Large Language Models) procesamiento grandes datos son priorizados. Discusión: consecuentes temática incremental académica. viene desarrollando ampliamente conceptos se proyecta consolidación Conclusiones: construcción Colombia tiene un importante camino emergentes (Inteligencia artificial), sin embargo, existe disposición para realizarlo aplicarlos sus diferentes ciclos vida proyecto.

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Towards the Development of a Budget Categorisation Machine Learning Tool: A Review

Lecture notes in civil engineering, Journal Year: 2022, Volume and Issue: unknown, P. 101 - 110

Published: Nov. 19, 2022

Language: Английский

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