Machine Learning and Artificial Intelligence Applications in Building Construction: Present Status and Future Trends

Ensafi, Mahnaz; Alimoradi, Saeid; Gao, Xinghua; Thabet, Walid

doi:10.1061/9780784483961.013

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Due to the challenges with decision-making methods and to benefit from historical data collected, the authors are planning to implement DDD, specifically neural network, to automate the prioritization of future work orders. Machine learning (ML) methods and algorithms have been used by different researchers in various fields including construction (Ensafi et al , 2022) and FM (Zarindast and Wood, 2021; Canizo et al , 2017) to address the challenges with existing practices. Having access to historical maintenance data and work order history can assist FMs with processing future work orders as well as determining correlations between different factors.…”

Section: Discussionmentioning

confidence: 99%

Investigation of work order processing in different facilities: a questionnaire-based survey

Ensafi,

Thabet,

Besiktepe

2024

Self Cite

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Purpose The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results. Design/methodology/approach This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance. Findings The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs. Originality/value Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

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Section: Discussionmentioning

confidence: 99%

Investigation of work order processing in different facilities: a questionnaire-based survey

Ensafi,

Thabet,

Besiktepe

2024

Self Cite

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“…Recently, an increasing number of studies are adopting AI techniques and ML algorithms to address design (Feng et al, 2019), construction (Ensafi et al, 2022), and facility management challenges (Awada et al, 2020;Feng et al, 2017;Tabrizi et al, 2012). Researchers have implemented AI and ML to address different aspects of building operation phase including air quality (Xie et al, 2020), energy consumption (Hajj-Hassan et al, 2020;Lu & Feng, 2020), and cost analysis (Liu et al, 2020;Gao et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Work order prioritization using neural networks to improve building operation

2024

ITcon

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Current practices for prioritizing maintenance work orders are mainly user-driven and lack consistency in collecting, processing, and managing the large amount of data. While decision-making methods have been used to address some of the existing challenges such as inconsistency, they also have challenges including variation between comparison during the actual prioritization task as opposed to those outside of maintenance context. The data analytics and machine learning methods can help with extracting meaningful and valuable information, finding patterns, and drawing conclusions from the available data. Such methods have benefits including faster prioritization performance leading to less failure and downtimes, reduced impact of knowledge loss, decreased cognitive workload, identification of errors for adjusting the system, and determination of important factors impacting work order processing to support the development of data requirements. This paper summarizes the background on existing gaps in processing maintenance work orders and provides an overview of machine learning methods to support prioritizing work order. The paper then discusses the work order data of an educational facility as a case study, presents information on data exploration and data cleaning approach, and provides insights gained from their maintenance work order data. The insights gained present challenges such as submission of multiple work orders as one, missing data for certain criteria, long durations for addressing some of the work orders, and the correlation between criteria collected by the facility and the schedule. The paper continues by implementing artificial neural networks to benefit from work order data collected for automatically prioritizing the future work orders. The results present the optimum neural network structure based on mean squared error estimated and provides the best value for each parameter used for the development of the model. The accuracy and efficiency of the developed model was validated by the facility experts of the educational facility.

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Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity

Efekan,

Celik,

Tokdemir

2023

Lecture Notes in Civil Engineering

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Machine Learning and Artificial Intelligence Applications in Building Construction: Present Status and Future Trends

Cited by 6 publications

References 37 publications

Investigation of work order processing in different facilities: a questionnaire-based survey

Investigation of work order processing in different facilities: a questionnaire-based survey

Work order prioritization using neural networks to improve building operation

Using Hybrid Artificial Intelligence Optimization Method to Predict Construction Labour Productivity

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