Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

Rampini, Luca; Cecconi, Fulvio Re

doi:10.36680/j.itcon.2022.043

Cited by 21 publications

(21 citation statements)

References 168 publications

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“…Risk management is a discipline that allows for "reducing financial losses, improving health and safety, goodwill and reputation, minimizing environmental and social impact, can result in reduced liabilities such as insurance premiums, fines and penalties". Most of the evaluations in Risk Management are subjective and based on experience; therefore, much research focuses on establishing a methodology for making objective, data-driven decisions and adopting AI solutions [1]. Case-Based Reasoning is a general term in project risk management for solving new problems based on similar past experiences.…”

Section: E Risk Managementmentioning

confidence: 99%

“…Despite a retarded initial adoption pace, construction leaders are beginning to take a greater interest in the transformative prospects of AI tech. During the next upcoming years, expect an increasingly quick rate for tech acceptance as applications and products targeted for construction continue hitting the market [1]. As a matter of fact, the construction industry is one of the least digitized industries in the world and most stakeholders acknowledge the age-long culture of resistance to change.…”

Section: Introductionmentioning

confidence: 99%

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A Review on Possibilities of Artificial Intelligence in construction Industry

Bhadoriya

2024

IJRASET

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At present if we see construction industries, they are facing lot of problems which finally create project delay. The progression of the construction industry is strictly limited by the countless complex challenges it faces such as cost and time overruns, health and safety, productivity and labour scarcities. Working on these complex problems of construction industries is not easy in many aspects but if these problems are countered with the help of AI results will be robust. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management processes. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning, pattern recognition, and deep learning have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. A present review of existing literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimization was conducted. Additionally, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study focuses on AI and its application in construction industry.

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Section: E Risk Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review on Possibilities of Artificial Intelligence in construction Industry

Bhadoriya

2024

IJRASET

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“…The use of synthetic data to improve the performance of a taught model has become a widespread practice in the computer vision and machine learning communities (Rampini and Cecconi, 2022). Because training a deep learning model generally necessitates a huge quantity of data, many academics have proposed using synthetic data to complement current data sets and offer training data for new applications.…”

Section: Synthetic Datamentioning

confidence: 99%

Synthetic images generation for semantic understanding in facility management

Rampini

Cecconi

2023

Self Cite

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Purpose This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model. Design/methodology/approach This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images. Findings The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects. Originality/value This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

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“…When an unpredictable fault occurs in a building, a maintenance process starts entailing a corrective action. In large and distributed buildings organizations, such as hospitals, universities and other public administrations, the number of contemporary corrective actions can be relevant, even if preventive maintenance approaches are in use (Dzulkifli et al, 2021;Ferreira et al, 2021;Hong et al, 2015;Rampini and Cecconi, 2022;Razali et al, 2020;Shalabi and Turkan, 2020). In these contexts, the maintenance process is usually managed by facility management (FM) contractors.…”

Section: Introductionmentioning

confidence: 99%

Influence of pre-processing methods on the automatic priority prediction of native-language end-users’ maintenance requests through machine learning methods

D’Orazio,

Bernardini,

Di Giuseppe

2024

ITcon

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Feedback and requests by occupants are relevant sources of data to improve building management, and building maintenance. Indeed, most predictable faults can be directly identified by occupants and communicated to facility managers through communications written in the end-users’ native language. In this sense, natural language processing methods can support the request identification and attribution process if they are robust enough to extract useful information from these unstructured textual sources. Machine learning (ML) can support assessing and managing these data, especially in the case of many simultaneous communications. In this field, the application of pre-processing and ML methods to English-written databases has been widely provided, while efforts in other native languages are still limited, impacting the real applicability. Moreover, the performance of combinations of methods for pre-processing, ML and classification classes attribution, has been limitedly investigated while comparing different languages. To fill this gap, this work hence explores the performance of automatic priority assignment of maintenance end-users’ requests depending on the combined influence of: (a) different natural language pre-processing methods, (b) several supervised ML algorithms, (c) two priority classification rules (2-class versus 4-class), (d) the database language (i.e. the original database written in Italian, the native end-users’ language; a translated database version in English, as standard reference). Analyses are performed on a database of about 12000 maintenance requests written in Italian concerning a stock of 23 buildings open to the public. A random sample of the sentences is supervised and labelled by 20 expert annotators following the best-worst method to attribute a priority score. Labelled sentences are then pre-processed using four different approaches to progressively reduce the number of unique words (potential predictors). Five different consolidated ML methods are applied, and comparisons involve accuracy, precision, recall and F1-score for each combination of pre-processing action, ML method and the number of priority classes. Results show that, within each ML algorithm, different pre-processing methods limitedly impact the final accuracy and average F1-score. In both Italian and English conditions, the best performance is obtained by NN, LR, SVM methods, while NB generally fails, and by considering the 2-class priority classification scale. In this sense, results confirm that facility managers can be effectively supported by ML methods for preliminary priority assessments in building maintenance processes, even when the requests database is written in end-users’ native language.

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Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

Cited by 21 publications

References 168 publications

A Review on Possibilities of Artificial Intelligence in construction Industry

A Review on Possibilities of Artificial Intelligence in construction Industry

Synthetic images generation for semantic understanding in facility management

Influence of pre-processing methods on the automatic priority prediction of native-language end-users’ maintenance requests through machine learning methods

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