Development of a BIM-based holonic system for real-time monitoring of building operational efficiency

doi:10.1007/s42524-019-0037-0

Front. Eng

2020, Vol. 7

Issue (1) : 89-103 https://doi.org/10.1007/s42524-019-0037-0

RESEARCH ARTICLE

Development of a BIM-based holonic system for real-time monitoring of building operational efficiency

Alessandro CARBONARI¹, Leonardo MESSI¹(

), Berardo NATICCHIA¹, Massimo VACCARINI¹, Massimiliano PIRANI²

¹. Department of Civil and Building Engineering and Architecture (DICEA), Polytechnic University of Marche, via Brecce Bianche n. 12, Ancona, Italy
². Department of Information Engineering (DII), Polytechnic University of Marche, via Brecce Bianche n. 12, Ancona, Italy

Download: PDF(2816 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

In the wide context of facility management, several processes, such as operations, maintenance, retrofitting, and renovations, ensure that buildings comply with the principles of efficiency, cost-effectiveness, and indoor comfort. Apart from ordinary operation, facility management is responsible for the renovation of and long-term performance improvement of building facilities. In such a scenario, the cyber–physical system (CPS) paradigm with holonic architecture, which is the focus of this study, can successfully guide the operation management and long-term refurbishment processes of buildings. Analogous to the manufacturing field, the developed CPS maximizes holons’ self-configuration and self-organization and overall throughput effectiveness metrics to detect the best corrective actions toward system improvements. Consequently, suggestions and lessons learned from the evaluation of building efficiency are redirected to the building information model. Hence, the digital model acts as a repository of currently available equipment for operations management and the history of diagnoses that support decision-making during the maintenance, retrofitting, and renovation processes. Evidently, the repeated detection of a specific issue, which is unaffected by operations management, should be considered an opportunity to act and enhance the performances of existing building components. Similar to a goods-producing industry, the building management system developed in this study applies the aforementioned methodology to provide services related to indoor comfort and building health. This approach indicates that a method for automatic real-time diagnosis is tested in a case study consisting of a multi-use and large public building. The current paper, which is an extended version of the one presented in the Creative Construction Conference 2018, deepens the decision support tool and the supervision policy. Moreover, the developed system is contextualized by providing an example of use case and highlighting the step forward in the field of smart buildings.

Keywords BIM building management system cyber-physical system facility management holonic system

Corresponding Author(s): Leonardo MESSI

Just Accepted Date: 05 May 2019 Online First Date: 18 June 2019 Issue Date: 02 March 2020

Cite this article:

Alessandro CARBONARI,Leonardo MESSI,Berardo NATICCHIA, et al. Development of a BIM-based holonic system for real-time monitoring of building operational efficiency[J]. Front. Eng, 2020, 7(1): 89-103.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-019-0037-0
https://academic.hep.com.cn/fem/EN/Y2020/V7/I1/89

Fig.1 Architecture of the developed holonic computing structure based on the CPS technology.

Fig.2 Results of the operation management for June. Trends of (a) temperature, (b) relative humidity, (c) CO₂ concentration vs number of people inside room no. 90, (d) shading level, (e) fan coil level, and (f) window level.

Fig.3 Use case of the holonic system for operational and renovation management.

Fig.4 Four unique subsystems. Adapted from ^{Muthiah and Huang (2007) by permission of Taylor & Francis Ltd.}

Interconnection type	OTE of parent holon	R_th of parent holon	Q_eff of parent holon
Series	$min {min i = 1, ..., n − 1 {O T E i ? ? ? R t h, i ? ? ∏ j = i + 1 n Q e f f, i}, O T E n ? ? ? R t h, n} min i = 1, ..., n {R t h, i}$	$min i = 1, ..., n {R t h, i}$	$∏ i = 1 n Q e f f, i$
Parallel	$(∑ i = 1 n O T E i ? R t h, i) / ∑ i = 1 n R t h, i$	$∑ i = 1 n R t h, i$	$∑ i = 1 n Q e f f, i n$
Assembly	$min {min i = 1, ..., n {O T E i ? R t h, i ? Q e f f, a / k a, i}, O T E a ? R t h, a} min {min i = 1, ..., n {R t h, i / k a, i}, R t h, a}$	$min {min i = 1, ..., n {R t h, i k a, i} ?, R t h, a ?}$	$∑ i = 1 n k a, i Q e f f, i ∑ i = 1 n k a, i Q e f f, a$
Expansion	$Σ i = 1 n min {R t h, e O T E e ? k e, i ? Q e f f, i, R t h, i ? O T E i} Σ i = 1 n min {R t h, e ? k e, i, R t h, i}$	$Σ i = 1 n min {R t h, e ? k e, i ? R t h, i}$	$∑ i = 1 n k e, i Q e f f, i ∑ i = 1 n k e, i$

Tab.1 Computing formulas of the OTE metrics in recursive form. Copyright(2016) IEEE. Adapted with permission from ^{Pirani et al. (2016)}

Fig.5 (a) System’s scheme and (b) system’s tree developed for the case study; (c) enlarged view of the active thermal source subsystem from the system’s tree.

Fig.6 (a) Regular-shaped room and (b) parallel interconnection compared with (c) irregular-shaped room and (d) assembly interconnection.

Fig.7 DST–bRM and bRM–bDM connections.

Tab.2 Queries to process and link data in the SQL environment

Fig.8 (a) Results of “Filter by #cell=9” query applied inside SQLiteStudio^® workspace (DST) and (b) “Copy table” and “Compile OEE mean value” queries applied inside SQL Server^® (bRM).

Fig.9 (a) OEE monthly mean value for June displayed inside Autodesk^® Revit^® as a shading’s parameter and (b) the room no. 90 in a 3D view of the Eustachio’s building information model (bDM).

Fig.10 OEE monthly mean values for June and July 2016.

1	A Akanmu, C J Anumba (2015). Cyber-physical systems integration of building information models and the physical construction. Engineering, Construction, and Architectural Management, 22(5): 516–535 https://doi.org/10.1108/ECAM-07-2014-0097
2	A Bonci, M Pirani, S Longhi (2017). Robotics 4.0: Performance improvement made easy. In: Proceedings of the 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, Cyprus https://doi.org/https://doi.org/10.1109/ETFA.2017.82 47682
3	A Borrmann, M König, C Koch, J Beetz (2018). Building information modeling: Why? What? How? In: Building Information Modeling, Cham: Springer, 1–24 https://doi.org/https://doi.org/10.1007/978-3-319-92862-3_1
4	S Bruno, M De Fino (2018), F Fatiguso. Historic Building Information Modelling: Performance assessment for diagnosis-aided information modelling and management. Automation in Construction, 86: 256–276 https://doi.org/10.1016/j.autcon.2017.11.009
5	H Darwen (2009). An Introduction to Relational Database Theory. London: Bookboon
6	T Eastman, L Sacks (2011). BIM Handbook—A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors. Hoboken: Wiley
7	A Giret, V Botti (2004). Holons and agents. Journal of Intelligent Manufacturing, 15(5): 645–659 https://doi.org/10.1023/B:JIMS.0000037714.56201.a3
8	A Koestler (1967). The Gost in the Machine. New York: The Macmillan Company
9	X D Ma, R Cui, Y Sun, C G Peng, Z S Wu (2010). Supervisory and energy management system of large public buildings. In: Proceeding of the 2010 IEEE, International Conference on Mechatronics and Automation, Xi’an, China https://doi.org/https://doi.org/10.1109/ICMA.2010.55 89969
10	L Monostori, B Kádár, T Bauernhansl, S Kondoh, S Kumara, G Reinhart, O Sauer, G Schuh, W Sihn, K Ueda (2016). Cyber-physical systems in manufacturing. CIRP Annals, 65(2): 621–641 https://doi.org/10.1016/j.cirp.2016.06.005
11	K M N Muthiah, S H Huang (2007). Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection. International Journal of Production Research, 45(20): 4753–4769 https://doi.org/10.1080/00207540600786731
12	M Pirani, A Bonci, S Longhi (2016). A scalable production efficiency tool for the robotic cloud in the fractal factory. In: IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 6847‒6852 https://doi.org/https://doi.org/10.1109/IECON.2016.7793536
13	P Russell, D Elger (2008). The meaning of BIM. In: 26th eCAADe Conference Proceedings, Antwerpen, Belgium, 531–536
14	D Stadnicka, A Bonci, M Pirani, S Longhi (2017). Information management and decision making supported by an intelligence system in kitchen fronts control process. In: Burduk A, Mazurkiewicz D, eds. Intelligent Systems in Production Engineering and Maintenance—ISPEM 2017. Heidelberg: Springer, 249–259 https://doi.org/https://doi.org/10.1007/978-3-319-64465-3_25
15	D Stadnicka, M Pirani, A Bonci, R M C Ratnayake, S Longhi (2017). Self-similar computing structures for CPSs: A case study on POTS service process. In: Camarinha-Matos L, Afsarmanesh H, Fornasiero R, eds. Collaboration in a Data-Rich World. PRO-VE 2017. Heidelberg: Springer, 157–166 https://doi.org/https://doi.org/10.1007/978-3-319-65151-4_15
16	P Valckenaers, H Van Brussel (2016). Design for the Unexpected, from Holonic Manufacturing Systems towards a Humane Machatronics Society. London: Butterworth-Heinemann
17	P Verstraete, B S Germain, K Hadeli, P Valckenaers, H Van Brussel (2006). On applying the PROSA reference architecture in multi-agent manufacturing control applications. In: Multiagent Systems and Software Architecture, Proceedings of the Special Track at Net.ObjectDays, K.U. Leuven, Belgium
18	R Volk, J Stengel, F Schultmann (2014). Building Information Modeling (BIM) for existing buildings—Literature review and future needs. Automation in Construction, 38: 109–127 https://doi.org/10.1016/j.autcon.2013.10.023
19	L H Wang, A Haghighi (2015). Combined strength of holons, agents and function blocks in cyber-physical systems. Journal of Manufacturing Systems, 40(2): 25–34 https://doi.org/10.1016/j.jmsy.2016.05.002
20	M Wetter, W Zuo, T S Nouidui, X F Pang (2014). Modelica buildings library. Journal of Building Performance Simulation, 7(4): 253–270 https://doi.org/10.1080/19401493.2013.765506
21	X Yuan, C J Anuba, M K Parfitt (2016). Cyber-physical systems for temporary structure monitoring. Automation in Construction, 66: 1–14 https://doi.org/10.1016/j.autcon.2016.02.005

[1]	Jordan DAVIDSON, John FOWLER, Charalampos PANTAZIS, Massimo SANNINO, Jordan WALKER, Moslem SHEIKHKHOSHKAR, Farzad Pour RAHIMIAN. Integration of VR with BIM to facilitate real-time creation of bill of quantities during the design phase: A proof of concept study[J]. Front. Eng, 2020, 7(3): 396-403.
[2]	Elodie HOCHSCHEID, Gilles HALIN. Generic and SME-specific factors that influence the BIM adoption process: An overview that highlights gaps in the literature[J]. Front. Eng, 2020, 7(1): 119-130.
[3]	Veronika BOLSHAKOVA, Annie GUERRIERO, Gilles HALIN. Identifying stakeholders’ roles and relevant project documents for 4D-based collaborative decision making[J]. Front. Eng, 2020, 7(1): 104-118.
[4]	Berardo NATICCHIA, Alessandra CORNELI, Alessandro CARBONARI. Framework based on building information modeling, mixed reality, and a cloud platform to support information flow in facility management[J]. Front. Eng, 2020, 7(1): 131-141.
[5]	Ying ZHOU, Wan WANG, Hanbin LUO, Yan ZHANG. Virtual pre-assembly for large steel structures based on BIM, PLP algorithm, and 3D measurement[J]. Front. Eng, 2019, 6(2): 207-220.
[6]	Rafaela BORTOLINI, Núria FORCADA. Facility managers’ perceptions on building performance assessment[J]. Front. Eng, 2018, 5(3): 324-333.
[7]	Albert P. C. CHAN, Xiaozhi MA, Wen YI, Xin ZHOU, Feng XIONG. Critical review of studies on building information modeling (BIM) in project management[J]. Front. Eng, 2018, 5(3): 394-406.
[8]	Marcel JOLY, Darci ODLOAK, Mario Y. MIYAKE, Brenno C. MENEZES, Jeffrey D. KELLY. Refinery production scheduling toward Industry 4.0[J]. Front. Eng, 2018, 5(2): 202-213.
[9]	Gang HUANG, Jianhui WANG, Chen CHEN, Chuangxin GUO, Bingquan ZHU. System resilience enhancement: Smart grid and beyond[J]. Front. Eng, 2017, 4(3): 271-282.
[10]	Jiming WANG. Theoretical research and application of petrochemical Cyber-physical Systems[J]. Front. Eng, 2017, 4(3): 242-255.
[11]	Ting Gong,Jian Yang,Hao Hu,Feng Xu. Construction Technology of Off-Site Precast Concrete Buildings[J]. Front. Eng, 2015, 2(2): 122-124.
[12]	Chong-guang Feng,Hao Hu,Feng Xu,Jian Yang. An Intelligent Logistics Management Model in Prefabricated Construction[J]. Front. Eng, 2015, 2(2): 178-181.
[13]	Burcu Akinci. Situational Awareness in Construction and Facility Management[J]. Front. Eng, 2014, 1(3): 283-289.

Viewed

Full text

Abstract

Cited

Shared

Discussed