Clash Analysis as a BIM Implementation Pre-Construction phase on Construction Project
Fahmi, Dwi kurniawan
Department of Civil Engineering, Mercu Buana University Jakarta, Indonesia
[email protected]
Abstract
Construction projects are complex activities because they have complexity in every aspect. The complexity contained in the construction project allows clashs between all aspects of the building. Miscommunication of planning drawings has the potential for clash between systems in the building. This clash has the potential to cause obstacles to implementation work in the field as well as rework. BIM's ability to detect early potential clashs between structural planning, architecture and MEP makes BIM potentially able to reduce the occurrence of rework or additional work. By using a case study of the Arumaya Residences Apartment project which consists of 4 basements and 23 floors, this research will focus on identifying potential clashs with the application of BIM in Architectural, Structural and MEP planning before construction work is carried out in the field. Clash analysis was carried out using BIM-based software (Revit and Navisworks). From the analysis results obtained 51 clashs with structural details with MEP 34 clashs, architects with MEP 13 clashs, architects with structures 2 clashs and MEP 2 clashs. Then the average time for resolving clashs based on each discipline that has been validated by experts is obtained as follows: structure with MEP: 4.73 hours/clash, architecture with MEP: 3.87 hours/clash, architecture with structure: 4.76 hours/clash and MEP : 3.69 hours/clash. And the factors that influence the results of BIM implementation in detecting clash are time, quality, and communication factors.
Keywords:
Building Information Modeling (BIM), Clash Detection, High rise Building.
1. Introduction
Currently the world is experiencing the fourth industrial revolution, also known as industry 4.0. The fourth industrial revolution has started since the beginning of the 21st century. Industry 4.0 produces smart factories with modular structures, cyber-physical systems that monitor physical processes and create virtual copies of the physical world. This trend is characterized by the Internet of Things (IoT), cyber-physical systems that communicate and cooperate with each other simultaneously. The Industrial Revolution 4.0 is a challenge that must be faced by all elements in the country. Especially in the construction industry, BIM (Building Information Modeling) appears to facilitate problem solving in all aspects of integrated construction. (Isneini, 2018)
In its development, innovation in high-rise building construction technology has become one of the challenges for civil engineering engineers to improve the quality of performance in the face of global competition.
High-rise building construction work has many complex sub-works. (Amin & Korniawan, 2016)
One of the technologies that has recently continued to develop in the architectural, engineering and construction industrial sectors is Building Information Modeling or also known as BIM. BIM technology is a technology that helps the process of building construction. Countries in Southeast Asia have used BIM technology in construction practice, but in Indonesia the use of BIM technology is still not widely used. With the government regulation no 2 of 2017 article 5 paragraph 5 which regulates the development of technology related to the development process, it will affect contractors as actors in construction services. Therefore, this study wants to see how contractors implement BIM technology, especially at the pre-construction stage. (Nelson & Tamtana, 2019)
Construction projects are complex activities because they have complexity in every aspect. All aspects of the building, starting from the structural aspect, architectural aspect, and MEP aspect, have the same importance and overlap with each other. The complexity contained in the construction project allows clashs between all aspects of the building. The design planning of the building aspect is contained in a work plan drawing. Work plan drawings (shopdrawing) are one of the communication tools used between construction actors in the process of carrying out construction projects. Miscommunication of planning drawings has the potential for clash between systems in the building. This clash has the potential to cause obstacles to implementation work in the field as well as rework. This can be detrimental to construction actors both in time and cost. BIM's ability to detect early potential clashs between structural planning, architecture and MEP makes BIM potentially able to reduce the occurrence of rework or additional work. By using a case study of the Arumaya Residences Apartment project which consists of 4 basements and 23 floors, this research will focus on identifying potential clashs with the application of BIM in Architectural, Structural and MEP planning before construction work is carried out in the field.
BIM (Building Information Modeling) is an approach to design building, construction, and management.
The scope of this BIM supports from design projects, schedules, and other information in a well-coordinated manner. BIM services provide the potential to model virtual information in a single model offering visualization, Clash detection, construction phase, and materials as well as model testing to be submitted from the design team (architects, surveyors, consulting, etc.) to contractors and sub-contractors and owners. (Rayendra, 2014) specifications may be searchable and linked to regional, national standards and international.
The advantages of BIM services according to (Rayendra, 2014)as follows:
1. Minimize the design lifecycle by increasing collaboration between owners, consultants and contractors.
2. High quality and accuracy of documentation of the construction process.
3. BIM technology is used for the entire building life cycle, including facilities operation and maintenance.
4. Products with high quality and minimize the possibility of clash.
5. Cutting project costs and minimizing construction material waste.
6. Improve construction management.
2. Methodology
Figure 1. Research Flow Diagram
3. Result and Discussion
3.1. Analysis Clash with BIM Technology
Figure 2. Modeling BIM (Architect)
Figure 3. Modeling BIM (Structure)
Figure 4. Modeling BIM (MEP)
Based on clash analysis with BIM-based software (Naviswork), 51 clashs were found with the following details:
Table 1. Clash Result with BIM Technology No Clash based on each discipline Total clash
1 Struktur with MEP 34 Clash
2 Arsitek with MEP 13 Clash
3 Arsitek with struktur 2 Clash
4 MEP 2 Clash
Total 51 Clash
3.2. Analysis of the average time need to resolved clash
Based on the results of the distribution of questionnaires with a total of 31 respondents involved in the construction process on the Arumaya Residences project.
The following is the cumulative average time needed to resolve clash in each discipline based on the average time for each clash that has been analyzed:
Table 2. The result of the analysis of the average clash time of each discipline No Clash based on each discipline Total clash Cumulative Average per Clash
1 Struktur with MEP 34 Clash 161 Hours 4.73 Hours 2 Arsitek with MEP 13 Clash 50.4 Hours 3.87 Hours 3 Arsitek with struktur 2 Clash 9.52 Hours 4.76 Hours
4 MEP 2 Clash 7.39 Hours 3.69 Hours
3.3. Analysis of the dominant factors that influence the results of BIM implementation in detecting clash.
1. Validity test
Validity test is carried out to see whether or not a proposed variable is valid. The provisions of the validity test are the comparison of the calculated r value with the r table, where the calculated r value must be greater than the r table value (r count > r table). With N = 31, the value of r table is 0.355.
Table 3. Validity test Correlations
Total X
Pearson
Correlation Sig. (2-tailed) N X1 Able to detect clashs/errors early and be able to prevent
them
.510** .003 31
X2 Able to accelerate the duration of construction projects.. .568** .000 31 X3 Able to accelerate decision making by each construction
stakeholder (owner, contractor, consultant, etc.).
.690** .000 31
X4 Can Reduce construction project repair costs .560** .001 31
X5 Able to reduce field rework work .707** .001 31
X6 Able to discipline company performance and project quality.
.597** .000 31
X7 Able to share information completely and quickly. .647** .000 31 X8 Able to help improve communication so that errors can be
minimized.
.429* .016 31
X9 Able to build synergy between construction stakeholders (owner, contractor, consultant, etc.)
.599** .000 31
X10 Able to build trust and reduce risk. .601** .000 31
Total X 1 33
a. Correlation is significant at the 0.01 level (2-tailed).
b. Correlation is significant at the 0.05 level (2-tailed).
From table 3 the results of the instrument validity test for the variable X can be concluded that each variable has a Pearson Correlation value > from the r-table value = 0.355, it can be concluded that the variable is declared valid.
2. Reability test
After the validity test is carried out and all variables are declared valid, then these variables are included in the reliability test. The results of the reliability test can be seen in table 4 below.
Table 4. Reability test Cronbach's Alpha N of Items
.787 10
Based on Table 4, the Cronbach's Alpha value is 0.787. So, this value indicates the "Good" or "Good"
category with a range of Cronbach's Alpha values of 0.7 < a < 0.9. So it can be said that all data is said to be reliable.
3. Normality test
Normality test is a test to see the distribution of research data is normal / not. Normality testing carried out in this study was using the One Sample Kolmogrov-Smirnov method. This test was conducted on 10 variables that were valid and reliable. The data can be said to be normally distributed if the result of the significance value is greater than the probability value of 5% or 0.05. The results obtained can be seen in Table 5.
Table 5. Normality test
Unstandardized Residual
N 31
Normal Parametersa,b Mean .0000000
Std. Deviation .22816870
Most Extreme Differences Absolute .111
Positive .111
Negative -.071
Test Statistic .111
Asymp. Sig. (2-tailed) .200c,d
Based on Table 5, it can be seen that all variables have a significance level of 0.200, where the value is greater than 0.05, so that the data distribution can be said to be normally distributed.
4. Multiple linier regression test ( Coefficient of determination (R2) ) Table 6. Coefficient of determination
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .943a .889 .815 .173
g. Predictors: (Constant), X10, X4, X1, X8, X5, X9, X2, X6, X3, X7
Based on the results of table 6 above, the coefficient of determination R2 (Adjusted R Square) is 0.815 or 81.5%. This shows that the percentage contribution of the influence of the independent variable (X) to the dependent variable (Y) is 81.5% while the remaining 18.5% is influenced or explained by other variables not included in this study.
5. F test
Table 7. F test
Model Sum of Squares Df Mean Square F Sig.
1 Regression 6.374 10 .637 8.162 .000b
Residual 1.562 20 .078
Total 7.935 30
Based on Table 7 the calculated F value obtained is 8.162. with a significance of 0.000. known value to determine the value of F table is (10 ; 20). So that the F table value is 2,390. This shows that the calculated F value
> F table (8,162 > 2,390). In addition, the significance value is also smaller than 0.05, which has a significant effect.
So it can be concluded that the independent variables simultaneously affect the dependent variable.
6. T test
The T-test is used to determine whether the independent variable in the regression model (X1, X2, …., Xn) partially has a significant effect on the dependent variable (Y).
Table 8. T test
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) .455 .741 .615 .546
X1 .478 .104 .534 4.605 .000
X2 .211 .158 .243 1.971 .063
X3 -.527 .113 -.733 -4.645 .000
X4 .078 .081 .130 .962 .347
X5 .310 .119 .410 2.603 .017
X6 -.035 .101 -.050 -.349 .731
X7 .006 .137 .007 .044 .965
X8 -.161 .105 -.204 -1.528 .142
X9 .253 .092 .347 2.735 .013
X10 .212 .135 .198 1.571 .132
It is known that the value to find the value of t table is (0.025 ; 20). So that the value of t table is 2.086. This shows that the value of t count for X1, X5 and X9 is greater than t table. In addition, the significance value is also smaller than 0.05, which has a significant effect. So it can be concluded that the independent variables X1, X5 and X9 partially affect the dependent variable.
From the results of data collection and data analysis that has been described above, there are several important things taken, namely:
2. From 5 factors with 10 research variables, all variables were declared valid and reliable and the next analysis could be done.
3. From the results of the normality test, all the significance values of the Kolmorogorov-Smirnov Sig test on each variable are above 0.05, meaning that the data obtained are normally distributed data.
Data analysis was carried out using multiple linear regression analysis and t test. From the analysis of the 10 research variables, 3 dominant variables were obtained from the implementation results, namely:
X1: able to detect clashs/errors early and be able to prevent them (time factor).
X5: able to reduce rework work in the field (quality factor).
X9 : able to build synergy between construction stakeholders (communication factor).
7. Expert Validation
Table 9. Expert Validation result Respondent 1 No Clash based on each
discipline
Total
Clash Cumulative Average
per clash Expert Validation agree Disagree 1 Struktur with MEP 34 161 Hours 4.73 Hours 3 pakar 2 pakar 2 Arsitek with MEP 13 50.4 Hours 3.87 Hours 3 pakar 2 pakar 3 Arsitek with struktur 2 9.52 Hours 4.76 Hours 4 pakar 1 pakar
4 MEP 2 7.39 Hours 3.69 Hours 4 pakar 1 pakar
In your opinion, do you agree that the time factor (able to detect clashs/errors early and be able to prevent them), the quality factor (able to reduce rework work in the field) and the communication factor (able to build synergy between construction stakeholders) are factors that influence the results of BIM implementation in Arumaya Residences project?
Table 10. Expert Validation result Respondent 2
Expert 1 Expert 2 Expert 3 Expert 4 Expert 5
Time factor (able to detect clashs/errors early and
be able to prevent them)
Agree Agree Agree Agree Agree
Quality Factor (able to reduce rework
work in the field)
Agree Agree Agree Agree Agree
Comunication Factor (able to build synergy between construction
stakeholders)
Agree Agree Agree Agree Agree
4. Conclusion
Based on the results of research and discussion that have been described in the previous chapter, the following conclusions are obtained.
1. From the results of clash analysis using BIM-based software on the Arumaya Residences project, 51 clash were obtained with the following details:
Structure vs MEP : 34 Clash Architect vs MEP : 13 Clash Structure vs Architect : 2 Clash
MEP : 2 Clash
2. From the results of data processing and has been validated by experts. The average time needed for repairs is obtained in the event of a clash between Architectural, Structure, and MEP planning on the Arumaya Residences project.
Structure with MEP : 4.73 Hours/clash Architect with MEP : 3.87 Hours/clash Structure with Architect : 4.76 Hours/clash
MEP : 3.69 Hours/clash
3. Based on the results of data processing, and has been validated by experts, the most dominant factors that influence the results of BIM implementation in detecting clash between architectural planning, structure and MEP in the Arumaya Residences project are:
c. Time factor (able to detect clashs/errors early and be able to prevent them).
d. Quality factor (able to reduce rework in the field)
e. Communication factor (able to build synergy between construction stakeholders)
References
Amin, Mawardi, and Tatang Korniawan. 2016. “Analisis Produktivitas Pekerjaan Instalasi Façade Curtain Wall Unitized System Pada Proyek High-Rise Building Dengan Metode Simulasi Operasi Konstruksi Berulang (Cyclone).” Rekayasa Sipil.
Isneini, Martalia. 2018. “Penerapan Teknologi Konstruksi Menghadapi Revolusi Industri 4.0.” Kementerian PUPR.
Nelson, Nelson, and Jane Sekarsari Tamtana. 2019. “FAKTOR YANG MEMENGARUHI PENERAPAN BUILDING INFORMATION MODELING (BIM) DALAM TAHAPAN PRA KONSTRUKSI GEDUNG BERTINGKAT.” JMTS: Jurnal Mitra Teknik Sipil.
Rayendra, Biemo W. Soemardi. 2014. “Studi Aplikasi Teknologi Building Information Modeling Untuk Pra- Konstruksi.” Simposium Nasional RAPI XIII.
Biographies
Fahmi, Born in Jakarta, February 22, 1978. Lecturer in Civil Engineering, Mercu buana university. He holds a bachelor's degree in Civil Engineering from the National Institut sains dan Teknologi Nasional. Then obtained a Master's degree in Civil Engineering with a concentration in Construction Management from Universitas Pelita Harapan in 2016, with the thesis title Analysis of Delays in Budget Hotel Construction Projects in Jakarta. He teaches Soil Mechanics 1, Soil Mechanics 2, Prestressed Concrete Structures, Concrete Structures 2, Construction Methods and Heavy Equipment and Construction Management. Worked on construction projects since 2002 until now, as a contractor, consultant and developer..
Dwi Kurniawan, born in Yogyakarta, January 9, 1998, Student in Civil Engineering Study program, Mercu Buana University. At the previous vocational level, he majored in Engineering Drawing at a public school in Jogjakarta.
He is graduated from SMK in 2016 and then continued his studies at Mercu Buana University in 2017. he is also working at a private construction company as a design engineering staff.
