COVID-19 pandemic changed our way of working, limiting the usual physical attendance of working spaces. Despite the drastic reduction in the number of daily users due to the pandemic restrictions, working buildings were often kept open to provide services to internal and external users. Pandemic obliged to change operation and maintenance (O&M) plans, due to the increase of ventilation requirements and the reduction of other types of services, with a strong impact on cost and management. Now the pandemic is reducing its effects and is time to question the future asset of buildings’ O&M plans, based on the pandemic lesson. Data collected by Computerized Maintenance Management Systems (CMMS) during COVID-19 then become an important source of understanding the future management of working places. End-users’ maintenance requests are usually expressed by natural language, then a text mining approach can be a useful tool to discover hidden knowledge from unstructured data stored in CMMS. This study applies text mining methods, including sentiment analysis, to the field of building maintenance, with the scope to evaluate how COVID-19 changed some aspects of the facility management process, including users’ perception

Elisa Di Giuseppe

Gabriele Bernardini

Marco D'Orazio

English

IRIS UniversitÃ  Politecnica delle Marche

  3rd Building and Management International Conference 3er Congreso Internacional de Gestión en Edificación   BIMIC 2021  COVID-19 IMPACT ON END-USER’S MAINTENANCE REQUESTS. A TEXT MINING APPROACH 1Marco D’Orazio; 2Gabriele Bernardini; 3Elisa Di Giuseppe 1DICEA Department, Università Politecnica delle Marche; Ancona (Italy). E-mail: m.dorazio@univpm.it  2DICEA Department, Università Politecnica delle Marche; Ancona (Italy). E-mail: g.bernardini@univpm.it  2DICEA Department, Università Politecnica delle Marche; Ancona (Italy). E-mail: e.digiuseppe@univpm.it  Keywords: building management, work order, text mining, sentiment analysis  Abstract COVID-19 pandemic changed our way of working, limiting the usual physical attendance of working spaces. Despite the drastic reduction in the number of daily users due to the pandemic restrictions, working buildings were often kept open to provide services to internal and external users. Pandemic obliged to change operation and maintenance (O&M) plans, due to the increase of ventilation requirements and the reduction of other types of services, with a strong impact on cost and management. Now the pandemic is reducing its effects and is time to question the future asset of buildings’ O&M plans, based on the pandemic lesson. Data collected by Computerized Maintenance Management Systems (CMMS) during COVID-19 then become an important source of understanding the future management of working places. End-users’ maintenance requests are usually expressed by natural language, then a text mining approach can be a useful tool to discover hidden knowledge from unstructured data stored in CMMS.  Text mining is a fundamental tool to discover hidden knowledge from massive and complex data stored in databases or other information repositories [1]. Recently, sentiment analysis methodologies have been applied to analyze some aspects of the building management process [2]. Text mining techniques [3] were applied to identify the major treated topics related to the energy use and management of buildings. Loureiro and Alló [4] employed Natural Language Processing (NLP) tools, based on the National Research Center Canada lexicon (EmoLex). Sun et al. analyzed microblog posts to derive information about energy policies [6]. Liu and Hu performed sentiment analysis of the public attention status toward green buildings, based on Ekman’s six basic universal emotions [7]. NLP models were applied to the facility management of buildings, collecting sentiments and opinions from end-users, to improve the building operability and the cost of the management process [8], [9]. Bortolini and Forcada developed a methodology to determine the typical problems that end-users complain 289  3rd Building and Management International Conference 3er Congreso Internacional de Gestión en Edificación   BIMIC 2021 about the building systems and their perceived severity [10]. Gunay et al. analyzed operators’ work order descriptions in Computerized Maintenance Management Systems (CMMS), extracting information about failure patterns in building systems and components [11]. Bouabdallaoui et al. proposed a machine-learning algorithm based on NLP to manage day-to-day maintenance activities [12]. Sexton et al. Compared different NLP methodologies to extract keywords from maintenance Work orders [13]. Bardhan et al. employed two emotion lexicon databases to collect information from users’ groups regarding housing management in India [14]. Sun et al. calculated sentiment value on energy price policies, using polarity and intensity of sentiment words, based on the China HowNet Thesaurus.  In the field of building maintenance, sentiment analysis methodologies could be also useful to reduce the risk associated with delays in intervention. Indeed, they could extract useful information at the beginning of the process (at the end-users’ request moment) in order to categorize and prioritize the requests. However, users’ perception can affect this outcome, since requests’ features also depend on the psychophysical state of each person, due to contextual internal and external factors. In this sense, COVID-19 dramatically changed people perception in several aspects, comprising the interaction with buildings.  Hence, it is necessary to understand how COVID-19 altered users’ perception about Facility Management (FM) processes.  This study applies text mining methods, including sentiment analysis, to the field of building maintenance, with the scope to evaluate how COVID-19 changed some aspects of the facility management process, including users’ perception.  The impact of the COVID-19 pandemic on maintenance activities of a set of 23 university buildings has been evaluated through a text mining approach, applying TF and sentiment analysis methods. The end-users maintenance requests generated during the different pandemic phases have been analyzed and information was collected about the shifts recorded in the end-users’ maintenance requests in terms of amount, type and end-users’ perceptions. Basing on the textual classification performed by FM technicians involved in the process, the number of monthly WOs/m2 has been derived for each COVID period. A total of about 12000 end-users’ maintenance requests has been processed through R statistical software (rel 4.0.3). For each intervention category, the number of WOs/m2 has been also derived to evaluate possible shifts in their proportion and trends due to the pandemic. Then a Term Frequancy (TF) analysis has been performed on the end-user’s sentences in the WOs corpus, through KH Coder software to check the most frequent words describing the issue and the action required. After that, a hierarchical cluster analysis has been performed using the ward method and the Jaccard distance to obtain a visual association between the most frequent words into specific clusters (TF > 400), useful to evaluate the recognizability of the required intervention categories. Then, sentiment analysis methods based on polarized lexicons have been applied [10]. The unstructured textual part of WO’s dataset (e-mail or message text) has been analyzed through R statistics software, and the following packages: “Sentimentr” [36], “Syuzhet”, 290  3rd Building and Management International Conference 3er Congreso Internacional de Gestión en Edificación   BIMIC 2021 “SentimentAnalysis”, “Lexicon”, and “Vader” [37]. Polarity based and Valence-based lexicons have been considered [38], [39]. Finally, to compare the coherence between the lexicons in the attribution of the polarity score, correlation analysis has been performed, based on the Spearman non-parametric method, due to the verified non-normality of the samples. Statistical analysis was performed through the R statistical software and the “stats” package.  The results show that the pandemic caused a shift in the amount and type of requested interventions. Apart from the general reduction during the lock-down phase (Table 1), we observed changes in HVAC category, due to the interventions required for security reasons. However, COVID seemed to have a short term impact. The distribution of the interventions in the last two phases of the pandemic remains almost the same as the pre-covid situation. However, TF analysis showed shifts in the user perception. Similar results are derived from sentiment analysis, but it is evident that the application of current lexicons and methodology need improvements when applied to technical languages such as the ones of FM (Figure 1,2). In fact, Sentiment analysis is based on lexicons that contain in some cases only a limited number of related technical polarized terms, beiung developed starting from general purposes datasets. Then, future researches should improve them so as to ensure a possible application of these methodologies, and then to automatize some aspect of the facility management process (i.e. categorization of the end-users maintenance requests, priority assignation).  Table 1 Monthly mean number/m2 of end-users’ maintenance requests  Phase   Building components   Electrical   HVAC  1 0.0048 0.0069 0.0041 2 0.0016 0.0028 0.0015 3 0.0025 0.0036 0.0038 4 0.0045 0.0064 0.0033    Figure 1: Sentiment analysis performed through lexicons. (a) Pre-covid data – phase 1; (b) Lockdown data – phase 2 a; (c) Post-lockdown data – phases 3 and 4; (d) Total dataset. 291  3rd Building and Management International Conference 3er Congreso Internacional de Gestión en Edificación   BIMIC 2021   Figure 2 Correlogram of the scores attributed through the Sentiment Analysis.    References [1] H. Yan, N. Yang, Y. Peng, and Y. Ren, “Data mining in the construction industry: Present status, opportunities, and future trends,” Autom. Constr., vol. 119, no. May, p. 103331, 2020, doi: 10.1016/j.autcon.2020.103331. [2] M. Marzouk and M. Enaba, “Text analytics to analyze and monitor construction project contract and correspondence,” Autom. Constr., vol. 98, no. December 2017, pp. 265–274, 2019, doi: 10.1016/j.autcon.2018.11.018. [3] Z. Ding, Z. Li, and C. Fan, “Building energy savings: Analysis of research trends based on text mining,” Autom. Constr., vol. 96, no. September, pp. 398–410, 2018, doi: 10.1016/j.autcon.2018.10.008. [4] M. L. Loureiro and M. Alló, “Sensing climate change and energy issues: Sentiment and emotion analysis with social media in the U.K. and Spain,” Energy Policy, vol. 143, no. May, 2020, doi: 10.1016/j.enpol.2020.111490. [5] S. M. Mohammad and P. D. Turney, “Crowdsourcing a word-emotion association lexicon,” Comput. Intell., vol. 29, no. 3, pp. 436–465, 2013, doi: 10.1111/j.1467-8640.2012.00460.x. [6] Y. Sun et al., “Residents’ sentiments towards electricity price policy: Evidence from text mining in social media,” Resour. Conserv. Recycl., vol. 160, no. May, p. 104903, 2020, doi: 10.1016/j.resconrec.2020.104903. [7] X. Liu and W. Hu, “Attention and sentiment of Chinese public toward green buildings based on Sina Weibo,” Sustain. Cities Soc., vol. 44, no. October 2018, pp. 550–558, 2019, doi: 10.1016/j.scs.2018.10.047. [8] R. Bortolini and N. Forcada, “Facility managers’ perceptions on building performance assessment,” Front. Eng. Manag., vol. 0, no. 0, p. 0, 2018, doi: 10.15302/j-fem-2018010. 292  3rd Building and Management International Conference 3er Congreso Internacional de Gestión en Edificación   BIMIC 2021 [9] S. Madureira, I. Flores-Colen, J. de Brito, and C. Pereira, “Maintenance planning of facades in current buildings,” Constr. Build. Mater., vol. 147, pp. 790–802, 2017, doi: 10.1016/j.conbuildmat.2017.04.195. [10] R. Bortolini and N. Forcada, “Analysis of building maintenance requests using a text mining approach: building services evaluation,” Build. Res. Inf., vol. 48, no. 2, pp. 207–217, 2020, doi: 10.1080/09613218.2019.1609291. [11] H. B. Gunay, W. Shen, and C. Yang, “Text-mining building maintenance work orders for component fault frequency,” Build. Res. Inf., vol. 47, no. 5, pp. 518–533, 2019, doi: 10.1080/09613218.2018.1459004. [12] Y. Bouabdallaoui, Z. Lafhaj, P. Yim, L. Ducoulombier, and B. Bennadji, “Natural language processing model for managing maintenance requests in buildings,” Buildings, vol. 10, no. 9, pp. 1–12, 2020, doi: 10.3390/BUILDINGS10090160. [13] T. Sexton, M. Hodkiewicz, M. P. Brundage, and T. Smoker, “Benchmarking for keyword extraction methodologies in maintenance work orders,” Proc. Annu. Conf. Progn. Heal. Manag. Soc. PHM, pp. 1–10, 2018. [14] R. Bardhan, M. Sunikka-Blank, and A. N. Haque, “Sentiment analysis as tool for gender mainstreaming in slum rehabilitation housing management in Mumbai, India,” Habitat Int., vol. 92, no. September, p. 102040, 2019, doi: 10.1016/j.habitatint.2019.102040.   293

COVID-19 impact on end-user's maintenance requests. A text mining approach

https://iris.univpm.it/bitstream/11566/294451/2/Pagine%20estratte%20da%20BIMIC%202021%20Proceedings.pdf

COVID-19 impact on end-user's maintenance requests. A text mining approach

Abstract

Similar works

Full text

Available Versions

IRIS UniversitÃ Politecnica delle Marche