An intelligent fuzzy-based hybrid metaheuristic algorithm for analysis the strength, energy and cost optimization of building material in construction management

Ronghui, Song; Liangrong, Ni

doi:10.1007/s00366-021-01420-9

Cited by 11 publications

(4 citation statements)

References 132 publications

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“…Regarding the type of project to which ML and NLP techniques have been most applied, “highway construction” leads the way with six publications (Cao and Ashuri, 2020; Gaussmann et al , 2020; Xue et al , 2020; Moon et al , 2021a; Moon et al , 2021b; Suneja et al , 2021), followed by “composite slabs” (Elhegazy et al , 2021; Juszczyk, 2018a, 2018b) and “sports buildings” with two each (Juszczyk et al , 2018; Juszczyk et al , 2019). The remaining types of projects include: “wood construction” (Akanbi and Zhang, 2020), “stairs” (Zhang et al , 2018), “building renovation” (Cho et al , 2019), “educational buildings” (Yaqubi and Salhotra, 2019), “bridges and piers” (Dimitriou et al , 2018), “electrical installations” (Ronghui and Liangrong, 2021) and “aggregate pavement and bases” (Jeon et al , 2021a).…”

Section: Resultsmentioning

confidence: 99%

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Jacques de Sousa,

Poças Martins,

Sanhudo

et al. 2024

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Purpose This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding. Design/methodology/approach This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction. Findings This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools. Originality/value Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

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Section: Resultsmentioning

confidence: 99%

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Jacques de Sousa,

Poças Martins,

Sanhudo

et al. 2024

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“…The results are contrasted with what was shown by previous reviews, such as references [36,37,84], showing that there is a growing trend of research on the subject. This review provides the classification of the articles found by PMI knowledge areas (Project Management Institute 2017), finding that the greatest interest of the academic community is in cost management [53,58,64,74,79], quality management [12,73,77], time management [57,60,62,82], and, finally, scope management [11,81,87]. There is a knowledge gap in the use of emerging technologies (big data, data science, and artificial intelligence) in the areas of stakeholder management, communications, and human resources, at least in the construction and civil works sector.…”

Section: Discussionmentioning

confidence: 99%

“…This is also associated with the detection of anomalies in real time for the generation of alerts. Another aspect is that data science facilitates the management and analysis of information quality, ensuring that it is more accurate and useful for the project [53,61,64,73].…”

Section: Question 2-what Are the Most-used Methodological Routes And ...mentioning

confidence: 99%

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

Zabala-Vargas,

Jaimes-Quintanilla,

Jimenez-Barrera

2023

Buildings

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

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“…In another study, Ronghui and Liangrong [67] presented a fuzzy-based hybrid metaheuristic algorithm for establishinig the cost optimization of building materials in construction projects.…”

Section: Planning Pdmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

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

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An intelligent fuzzy-based hybrid metaheuristic algorithm for analysis the strength, energy and cost optimization of building material in construction management

Cited by 11 publications

References 132 publications

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

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

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

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