Exploring Macro BIM for Cost Prediction: Leveraging Similarity Assessment Methods and Market Data

Hanna PACYNO; Michał JUSZCZYK

doi:10.2478/acee-2024-0022

Exploring Macro BIM for Cost Prediction: Leveraging Similarity Assessment Methods and Market Data

Architecture, Civil Engineering, Environment

Volume 17 (2024): Issue 3 (September 2024)

By: Hanna PACYNO and Michał JUSZCZYK

Open Access

|Jan 2025

Figures & Tables

The exemplary level of Macro BIM model detailing. Graphic source: [5]

Macro BIM process scheme. Authors’ study based on [5]

The scheme presents the objectives and following steps of case study

Table 1.

List of primary school facilities. Selected data and cost values [PLN] per unit [m³]

ID	Value of works according to the investor / initial estimated value [PLN]	Contract price [PLN]	Building volume [m³]	Investment value / volume [m³]	Contractual price per volume (price per m²)	Object size according to area [m²] (S/M/L)	Number of storeys (O/F/M)	Location	Location Type (Rural, Urban)
LP3	43638488.13	46358700.00	37536.54	1162.56	1235.03	L (6 166.40 m²)	F-2	Środa Śląska	U
LP13	9310000.00	10091905.01	7244.94	1285.03	1392.96	M (1269.67 m²)	F-2	Dopiewo	R
LP53	48474598.56	50899568.95	38422.20	1261.63	1324.74	L (7110.20 m²)	F-3	Warszawa	U
LP103	60121311.66	68861788.62	45515.50	1320.90	1512.93	L (11957.30 m²)	F-3	Warszawa	U
LP107	11685000.00	13499681.03	25739.00	453.98	524.48	M (4360.24 m²)	F-2	Skokowa	R
LP113	72281756.60	74057723.57	49916.50	1448.05	1448.05	M (11737.00 m²)	F-2	Warszawa	U
LP122	2212520.91	2691987.73	1229.00	1800.26	2190.39	S	O-1	Czarnów	R
LP123	56910569.11	62448078.09	-	-	-	L	M-3	Toruń	U

An example of a Macro BIM model divided into grouped functions

An example of a Macro BIM model divided by floors

Table 2.

Comparison of geometric values of reference objects compared to the sample (differences)

Sample:
LP53	38 422.20		L (7110.2 m²)		F - 3		Warszawa	U
reference objects (compared):
ID	Building volume [m³]	Percentage of diff. of the volume criterion from the sample	Object size according to area [m²] (S/M/L)	Percentage of diff. of the surface criterion from the sample	Number of storeys (O/F/M)	The diff. in the number of storeys in relation to sample	Location	Location Type (Rural. Urban)	Characteristics of the place
LP3	37536.54	2.31	L (6 166.40 m²)	13.27	F - 2	diff.: 1	Środa Śląska	U	urban
LP13	7244.94	81.14	M (1269.67 m²)	82.14	F - 2	diff.: 1	Dopiewo	R	rural
LP103	45515.50	18.46	L (11957.30 m²)	68.17	F - 3	the same	Warszawa	U	same (the capital)
LP107	25739.00	33.01	M (4360.24 m²)	38.68	F - 2	diff.: 1	Skokowa	R	rural
LP113	49916.50	29.92	M (11737.00 m²)	65.07	F - 2	diff.: 1	Warszawa	U	same (the capital)
LP122	1229.00	96.80	S	huge diff.	O - 1	diff.: 2	Czarnów	R	rural
Lp123	-	-	L	-	M - 3	the same	Toruń	U	urban

Table 3.

Sorting by ranking according to the smallest differences for each 4 criteria. Analysis of the best (smallest) results in terms of volume and area values as well as the number of floors and location

	scale 1-6	scale 1-6	scale 1-3	scale 1-3
ID	Rank position (smallest diff.) by volume criterion	Rank position (smallest diff.) by area criterion	Ranking position of the number of storeys compliance	Location type compatibility ranking	volume and area criterion	The criterion of the number of storeys and location
LP3	1	1	2	2	2	4
LP13	5	5	2	3	10	5
LP103	2	4	1	1	6	2
LP107	4	2	2	3	6	5
LP113	3	3	2	1	6	3
LP122	6	6	3	3	12	6
LP123	-	-	1	2	-	3

The two most similar Macro BIM models (LP3 and LP103) and the Macro BIM Sample model (LP53)

Table 4.

Basic data for calculations about the sample and reference objects

sample:
LP53	38 422.20	L (7110.2 m²)	F - 3	Warszawa	U
reference objects:
ID	Building volume [m³]	Object size according to area [m²] (S/M/L)	Number of storeys (O/F/M)	Location	Location Type (Rural, Urban, Capital)
LP3	37 536.54	L(6 166.40 m²)	F - 2	Środa Śląska	U
LP13	7244.94	M (1269.67 m²)	F - 2	Dopiewo	R
LP103	45 515.50	L (11957.3 m²)	F - 3	Warszawa	C (same as sample)
LP107	25 739	M (4360.24 m²)	F - 2	Skokowa. gmina Prusice	R
LP113	49 916.50	M (11737 m²)	F - 2	Warszawa	C (same as sample)
LP122	1 229	S *	O - 1	Czarnów. Gmina Górzyca	R
LP123	-	L	M - 3	Toruń	U

Table 5.

The notation of individual features and the resulting Euclidean distances for the objects (sample LP53 object – reference model indicated by ID)

	ID	Notation of individual features (vol.[m³], area [m²], encoded floors. encoded location)	Euclidean distance for the object
	LP53	38 422.2. 7 110.2. 0. 1. 0. 0. 0. 1	(sample object)
1.	LP3	37 536.54. 6 166.40. 0. 1. 0. 0. 1. 0	1 294.27
2.	LP13	7244.94. 1269.67. 0. 1. 0. 1. 0. 0	31 719.60
3.	LP103	45 515.50. 11957.3. 0. 1. 0. 0. 0. 1	8 591.23
4.	LP107	25 739. 4360.24. 0. 1. 0. 1. 0. 0	15 434.16
5.	LP113	49 916.50. 11737. 0. 1. 0. 0. 0. 1	12 390.57
6.	LP122	1 229. 500. 1. 0. 0. 1. 0. 0	37 776.04

Table 6.

Values before normalization

	Volume	Area	Storeys	Location
Sample	38 422.20	7110.2	1 (3)	1
LP3	37 536.54	6 166.40	1 (2)	0
LP13	7244.94	1269.67	1 (2)	0
LP103	45 515.50	11957.3	1 (3)	1
LP107	25 739	4360.24	1 (2)	0
LP113	49 916.50	11737	1 (2)	1
LP122	1 229	500	0(1)	0

Table 7.

Values after normalization

	Volume	Area	Storeys	Location
Sample	0.425208809	0.359952511	0.4082483	0.57735
LP3	0.415407433	0.312172817	0.4082483	0
LP13	0.080177926	0.0642768	0.4082483	0
LP103	0.503708573	0.605336019	0.4082483	0.57735
LP107	0.284847029	0.220736314	0.4082483	0
LP113	0.552413332	0.594183374	0.4082483	0.57735
LP122	0.013601033	0.025312404	0	0

Table 8.

Determining the importance of criteria – Matrix for weight calculations

	Volume	Area	Storeys	Location
Volume	1	2	3	3
Area	0.5	1	3	3
Storeys	0.333333	0.333333	1	0.5
Location	0.333333	0.333333	2	1

Table 9.

Application of the Manhattan Method on normalized values without taking into account weights. The table highlights the three best results

	Volume	Area	Storeys	Location	Distances
LP3	0.009801376	0.047779694	0	0.57735	0.634931339
LP13	0.345030883	0.295675711	0	0.57735	1.218056863
LP103	0.078499764	0.245383508	0	0	0.323883272
LP107	0.14036178	0.139216197	0	0.57735	0.856928246
LP113	0.127204523	0.234230862	0	0	0.361435385
LP122	0.411607775	0.334640107	0.4082483	0.57735	1.731846442

Table 10.

Application of the Manhattan Method on normalized values. including calculated weights. The table highlights the two best results

	Volume	Area	Storeys	Location	Distances
LP3	0.004264398	0.014773838	0	0.086647	0.105685637
LP13	0.150116583	0.091425135	0	0.086647	0.328189119
LP103	0.034153802	0.075874411	0	0	0.110028213
LP107	0.061068826	0.043046687	0	0.086647	0.190762913
LP113	0.055344345	0.07242593	0	0	0.127770275
LP122	0.179082963	0.103473217	0.0431246	0.086647	0.412328177

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/acee-2024-0022 | Journal eISSN: 2720-6947 (formerly 1899-0142) | Journal ISSN: 1899-0142

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Language: English

Page range: 71 - 83

Submitted on: Sep 22, 2023

Accepted on: Apr 25, 2024

Published on: Jan 9, 2025

Published by: Silesian University of Technology

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

Building information modeling (BIM),

Macro BIM,

Construction cost management,

Cost estimates

Related subjects:

Architecture and design,

Architecture,

Architects, buildings

© 2025 Hanna PACYNO, Michał JUSZCZYK, published by Silesian University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 17 (2024): Issue 3 (September 2024)

Exploring Macro BIM for Cost Prediction: Leveraging Similarity Assessment Methods and Market Data

Figures & Tables

Figure 1.

Figure 2.

Figure 3.

Table 1.

Figure 4.

Figure 5.

Table 2.

Table 3.

Figure 6.

Table 4.

Table 5.

Table 6.

Table 7.

Table 8.

Table 9.

Table 10.

Paradigm

My account