Flooding remains one of Malaysia’s critical natural hazards, and its vulnerability is expected to increase as climate change results in higher rainfall intensity and sea level rise. In the fast-developing urban area of Johor Bahru, these factors are further worsened by the highly dense infrastructure and large impermeable surfaces, leading to significant damage (Jabatan Pengairan dan Saliran Malaysia, 2022). While conventional flood management approaches are fundamental, they often struggle with inefficiencies and project delays in such a complex environment. As an alternative, BIM provides an integrated digital platform proven to enhance project coordination and reduce errors in major infrastructure works (Dehdasht et al., 2021).
Flooding is a recurring issue in Johor Bahru, causing significant damage to infrastructure, disrupting daily life, and leading to economic losses. Despite the ongoing mitigation efforts, Johor Bahru is continuing to experience significant flooding intensified by rapid urbanisation and climate change, leading to severe infrastructure damage and economic loss. According to the latest annual flood report from the Department of Irrigation and Drainage Malaysia, Johor state has recorded 92 cases of flooding, of which 10 cases of it contributed by Johor Bahru (JPS, 2022). Flood control has long been dependent on infrastructure such as dams, levees, widening rivers, and upgrading drains, and non-structural strategies like land-use planning, flood forecasting, warning, and public information. Although important, these techniques cannot work effectively in complex urban environments such as Johor Bahru, where standard construction and project managers have been the causes of inefficiencies, cost overruns, and delays in delivering projects. This underlines the necessity of new input, which can increase efficiency, collaboration, and sustainability. While BIM presents a powerful solution to address common project delivery challenges through enhanced simulation of flood and collaboration. However, its adoption within Malaysia’s construction sector remains inconsistent, and its application for flood mitigation is critically underutilised (Dehdasht et al., 2021). BIM also helps in early flood risk management by providing data for solution selection and cost estimation before construction begins. When integrated with GIS, BIM can simulate various flooding scenarios to inform effective designs. This approach improves financial and technical outcomes through enhanced planning and data management (Mayouf et al., 2022; Zima, 2025).
However, the current adoption level and the specific challenges of BIM for flood mitigation within Johor Bahru faced by local stakeholders remain unclear. This report addresses this gap by investigating strategies to improve BIM implementation for flood management, aiming to strengthen the city’s defenses against this recurring threat. Therefore, a significant gap exists between the recognised potential of BIM and its practical implementation. The objectives of the study are: (i) To determine the current levels of BIM adoption in flood mitigation, (ii) To investigate the barriers to BIM implementation in flood mitigation, and (iii) To propose strategies to enhance BIM adoption in flood mitigation. By achieving these objectives, this research addresses this gap by investigating the specific barriers and current adoption levels of BIM among industry professionals in Johor Bahru, aiming to propose strategies to help enhance the local flood resilience by utilising the BIM technology (Pawłowicz, 2020).
Building Information Modelling (BIM) is the utilisation of shared, information-rich 3D digital models by all stakeholders to achieve common goals throughout a project’s life cycle (CIDB, 2020; Patel, 2024). This integrated approach allows all parties to effectively plan, design, and manage data across the supply chain (CIDB, 2019). The growing importance of BIM is highlighted by its potential to reduce costly rework, which can account for up to 5 % of a project’s contract value. In a rapidly evolving construction sector, businesses that fail to adopt BIM risk falling behind their competitors.
Flood mitigation involves both structural and non-structural strategies to reduce flood risk and damage (UNDRR, 2017). BIM enhances flood mitigation by supporting early decision-making, cost estimation, and risk visualisation. When integrated with GIS, BIM can simulate flood scenarios, improve design, and strengthen collaboration among stakeholders (Abuzaid & Al-Falahat, 2025; Mayouf et al., 2022).
In 2022, Johor recorded a total of 92 flood events, comprising 62 flash floods, 24 monsoon floods, and 6 coastal floods. Johor Bahru district recorded the second-highest number of flood incidents in the state, with a total of 10 occurrences (Channel NewsAsia, 2023; JPS, 2022). In March 2025, Johor Bahru was among the worst-hit districts during widespread floods in Johor, with over 700 residents evacuated due to rising water levels (The Straits Times, 2025). These recurring incidents underscore the urgent need for sustainable flood management approaches, including the integration of digital tools such as BIM in planning and mitigation efforts.
The BIM report of 2016 states that 17 % of the country has adopted BIM in Malaysia. The adoption rate in 2019 is 49 %, which is a significant rise over 2016. The percentage of BIM adoption then rose continuously to 55 % in 2021. From 2016 to 2021, the respondent’s interest in implementing BIM increased gradually. It is proven that different organisation sizes have different BIM adopters. It reveals that the survey results are dominated by large organisations (30 %), followed by small organisations (25 %), and medium-to-large organisations (23 %) (Ismail et al., 2022; Latiffi et al., 2013; 2014; Rahman et al., 2021, Zakari et al., 2014). On top of that, the JKR Strategic Plan 2021–2025 has set the target of the adoption of BIM to achieve 80 % by year 2025 (CIDB, 2020).
There are a variety of barriers that hinder the widespread implementation of BIM in the construction industry. For example, the increment of construction cost, lack of BIM expertise, high software costs, high training costs, and high technology costs (Rahman et al., 2021). Furthermore, human-related factors such as cultural resistance to abandoning traditional methods, coupled with external issues like insufficient support and incentives from governing bodies, considerably slow down the overall adoption process (Zakari et al., 2014). In addition, institutional and policy barriers, such as the lack of formal and standard guidelines, limited coordination, and data sharing among local agencies. Besides, there is no centralised system for data exchange and a shortage of trained personnel to combine both BIM and flood engineering (Sa’adi et al., 2024).
A set of strategic approaches is vital to deal with the obstacles in the construction industry to achieve the success of widespread BIM adoption. According to Rahman et al. (2021), the BIM report 2021 highlights that the strategies to improve the adoption and implementation of BIM are to create BIM institutes for training new generations and promote strong leadership support to adopt BIM. Besides, to establish standardised guidelines for BIM adoption nationwide in Malaysia. CIDB (2020) also outlined the strategies to improve BIM adoption, such as a training hub at MyBIM Centre, introduced regulations such as mandates of BIM in public projects above a value of RM10 million in Malaysia, and proposed financial incentives and subsidies to ease the burden of initial investment of SME in BIM technology. Finally, a National BIM eSubmission (NBeS) is introduced to facilitate and standardise the submission, review, and approval of building plans using BIM models for local authorities and regulatory bodies. These comprehensive strategies reflected the commitment of CIDB to driving digital transformation and increasing BIM adoption across Malaysia’s built environment.
The methodology includes the research design, approach, and techniques applied to meet the objective of this study. The focus of this study is on the adoption and implementation of Building Information Modelling (BIM) for flood mitigation in Johor Bahru, investigating the barriers that exist, determining the current level of implementation, and proposing ways of promoting improvement. A questionnaire survey, interviews, and document review were conducted to study the adoption and implementation of BIM in the Johor Bahru area. Figure 1 shows the flow chart of the research methodology adopted in this study.
Flow chart of research methodology (own research)
Johor Bahru was selected as the research area for conducting the survey and questionnaire, as it is a fast-emerging city situated in the south of Malaysia. It is the capital city of Johor and has been one of the entrances to Singapore. Playing an important role in this respect, Bandaraya Johor Bahru has become important in the economic development of the nation. The questionnaire was distributed to the case study contractor company to assess their perceptions on the problems of adoption and implementation. This fieldwork has been done within a specified period considered sufficient for representation and the reliability of data collected to answer the research objective.
Sampling involves selecting a representative group of individuals from the overall population within the study’s scope. The sample size was carefully determined to ensure it is large enough to support the validity and reliability of the statistical analysis. For this study, the sample size will be calculated using Taro Yamane’s formula (Yamane, 1967), which applies a 95 % confidence level and a precision level (e) of 0.05. Yamane’s formula is listed below:
n – required sample size,
N – the population size.
Finding out the demographics of the respondents, predicting the likelihood that a specific value of a variable phenomenon would occur, and evaluating the prediction’s correctness are the goals of frequency analysis. The formula for frequency analysis is as follows:
Descriptive analysis is a data analysis procedure that summarises data points, therefore leading to constructive interpretation in understanding patterns and trends (Rahman et al., 2021). In this study, SPSS statistical packages have been utilised in conducting the descriptive analysis. First, the mean score of each question is computed from the five-point Likert scale responses. The various means allow the responses to be categorised to fit the answer levels. For example, the mean score from 1–5 represents the level of agreement of respondents, rating from strongly disagree to strongly agree accordingly.
Reliability is the internal consistency of the data collected – it means how stable and dependable the data are under constant conditions. In this respect, the Cronbach’s alpha coefficient was used for the reliability of the research data (Cronbach, 1951; SPSS Tutorials, 2025; SPSSAnalysis.com, 2025). The coefficient of Cronbach’s alpha ranges between 0 and 1. The Cronbach’s alpha value from 0–1 represents the level of data reliability, rating from unacceptable to excellent is listed in Table 1 (Cronbach, 1951).
Interpretation of Cronbach’s alpha value (Cronbach, 1951)
| Internal consistency | Cronbach’s alpha range |
|---|---|
| Excellent | α ≥ 0.9 |
| Good | 0.8 ≤ α < 0.9 |
| Acceptable | 0.7 ≤ α < 0.8 |
| Questionable | 0.6 ≤ α < 0.7 |
| Poor | 0.5 ≤ α < 0.6 |
| Unacceptable | α < 0.5 |
Mean score analysis was used to interpret respondents’ level of agreement with each statement in the questionnaire, based on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The interpretation was categorised using an interval of 0.8 for each range in Table 2.
Interpretation of mean score analysis (Hamid et al., 2022; Ahmad, 2002)
| Mean score | Mean score interpretation |
|---|---|
| 1.00–2.33 | low |
| 2.34–3.67 | moderate |
| 3.68–5.00 | high |
In SPSS, the variables require input data. In the “Data View” spreadsheet, columns are uniformly formatted, and variable names are generated by double-clicking the column heading. Variable names must start with a letter and can be in either uppercase or lowercase. The variable type has to be defined as “String” (text) or one of the numeric formats. If variables have multiple values, labels should be assigned. For instance, “0” can represent “No” and “1” can represent “Yes” by using the “Labels” submenu found under “Define Variable”. During data entry, numeric values such as “0” or “1” are used to represent the labels.
Each case is represented by a row, and values are entered cell by cell for accuracy. Make sure the data type matches the value; for example, placing a dollar amount in a date-formatted column generates an error. Moving on to the next row represents moving to the next case. Adding more variables is done by double-clicking on the next available column heading. Analysis can be conducted through SPSS once data is inputted; frequency tables, descriptive analyses, and a number of graph options are available.
A questionnaire survey was distributed to the respondents via email and WhatsApp to gather insights on their experience with BIM adoption in flood mitigation project designs. The questionnaire was developed based on the literature review. Descriptive statistics were applied to analyse the survey responses using the IBM SPSS software, with results presented in the form of tables. A total of 15 respondents participated in the survey.
This section presents the background information of the respondents for the understanding of respondent profiles. It is crucial to ensure that they are qualified to answer the questionnaire accurately and meaningfully. The details of the respondents’ backgrounds are summarised in Table 3.
Based on Table 3, the majority of respondents are male (86.7 %), while 13.3 % of them are female. The age distribution shows that 33.3 % of the respondents are between 26 to 35 years old, followed by 26.7 % in the age groups of 36–45 years. 20.0% of respondents recorded for both under 25 years old and above 45 years old. In addition, most of the respondents (73.3 %) held a Bachelor’s degree while 13.3 % of the respondents are recorded for Diploma qualification. A small proportion of respondents (6.7 % each) attained a Master’s degree and a PhD/Doctorate.
As for their professional roles, Civil Engineers is the most common role, recorded for 33.3 %, followed by Construction Managers (26.7 %). Next, the third-ranked professional role is Architects, accounting for 20.0 % of the participants. Then, 13.3 % of respondents are recorded for Project Engineer, while only one respondent (6.7 %) is recorded for Project Manager. On the other hand, an equal portion, 26.7 % of respondents, recorded for both “more than 15 years of experience” and “less than 2 years”. Followed by 20.0 % recorded for both “2–5 years” and “11–15 years of experience”, while only 6.7 % of respondents recorded 6–10 years of experience.
Demographic profile of respondents (n = 15) (own research)
| Demographic information | |||
|---|---|---|---|
| Respondent | Category | Frequency | Percentage [%] |
| Gender | Male | 13 | 86.7 |
| Female | 2 | 13.3 | |
| Age group | 26–35 years old | 5 | 33.3 |
| 36–45 years old | 4 | 26.7 | |
| Under 25 years old | 3 | 20.0 | |
| Above 45 years old | 3 | 20.0 | |
| Highest level of education | Bachelor’s Degree | 11 | 73.3 |
| Diploma | 2 | 13.3 | |
| Master’s Degree | 1 | 6.7 | |
| PhD / Doctorate | 1 | 6.7 | |
| Professional role | Civil Engineer | 5 | 33.3 |
| Construction Manager | 4 | 26.7 | |
| Architect | 3 | 20.0 | |
| Project Engineer | 2 | 13.3 | |
| Project Manager | 1 | 6.7 | |
| Years of professional experience | Less than 2 years | 4 | 26.7 |
| More than 15 years | 4 | 26.7 | |
| 2–5 years | 3 | 20.0 | |
| 11–15 years | 3 | 20.0 | |
| 6–10 years | 1 | 6.7 | |
The strong educational backgrounds of respondents, along with the diversity in roles, ages, and experience levels, provide a comprehensive perspective and valuable insights into existing practices and areas for potential improvement.
This section presents the results of the current levels of BIM usage for flood mitigation and BIM maturity level within the case study organisation in flood mitigation projects in Johor Bahru, based on responses from 15 participants. This section aims to assess the current levels of BIM for flood mitigation in the case study organisation. Table 4 shows the results of the responses using frequency analysis.
Frequency analysis of current levels of BIM adoption in flood mitigation (n = 15) (own research)
| Category | Frequency | Percentage [%] | Cumulative percentage [%] | |
|---|---|---|---|---|
| Frequency of BIM implementation in flood mitigation | Never | 3 | 20.0 | 20.0 |
| Rarely | 0 | 0.0 | 20.0 | |
| Sometimes | 2 | 13.3 | 33.3 | |
| Often | 3 | 20.0 | 53.3 | |
| Always | 7 | 46.7 | 100.0 | |
| Level of BIM maturity in flood mitigation projects | Level 3: Integrated BIM | 9 | 60.0 | 60.0 |
| Level 0: No BIM Use | 3 | 20.0 | 80.0 | |
| Level 1: Low Collaboration | 2 | 13.3 | 93.3 | |
| Level 2: Collaborative BIM | 1 | 6.7 | 100.0 |
Table 4 shows that the majority of the respondents (46.7 %) indicated that BIM is always used in their organisation. This suggests a strong level of commitment to BIM adoption in the G7-rated contractor company, which is a more advanced digitally integrated company. Furthermore, 20 % of respondents reported that BIM is never used, while another 13.3 % indicated that BIM is used sometimes. In addition, 20 % of the respondents reported using BIM often. The results might indicate a transitional stage in the organisations. Overall, more than two-thirds (66.7 %) of respondents reported frequent use of BIM (often and always), which indicated a positive trend in BIM adoption within the context of flood mitigation efforts in the case study organisation. However, the adoption rate of BIM has yet to reach the target set by CIDB Malaysia in the JKR Strategic Plan 2021–2025, which aims for 80% adoption by the year 2025 (CIDB, 2020).
On the other hand, the majority of respondents (60 %) indicated their organisation is at Level 3: Integrated BIM, which represents the most advanced level of BIM maturity where collaboration across disciplines is fully integrated into workflows and decision-making processes. This aligns with the high frequency of BIM usage reported above. Meanwhile, 20.0 % of respondents identified their organisations at Level 0 (No BIM Use) and another 13.3 % at Level 1 (Low Collaboration). Only 6.7 % of respondents reported their organisation being at Level 2: Collaborative BIM, and another 6.7 % are unsure of their BIM maturity level, which might indicate an implementation gap. The results demonstrate a significant concentration (60 %) at the highest level of BIM maturity, but also suggest that a portion of the respondents remain at lower BIM maturity levels. This disparity probably suggests the need for further strategies to enhance the adoption of BIM for flood mitigation within the organisation.
This section discusses the quantitative findings from the survey on the barriers to implementing Building Information Modelling (BIM) for flood mitigation within the case study organisation. The data was analysed using descriptive statistics, specifically the mean score and standard deviation, to identify and rank the significance of perceived barriers among the respondents. The results are summarised in Table 5.
Mean score of barriers to BIM implementation in flood mitigation (own research)
| Barriers to BIM implementation in flood mitigation | Mean score | Mean score interpretation | Standard deviation | Rank |
|---|---|---|---|---|
| High initial investment costs for BIM implementation in flood mitigation | 3.87 | high | 1.060 | 1 |
| Limited access to accurate flood and terrain data for BIM modelling in flood mitigation | 3.73 | high | 0.961 | 2 |
| Lack of expertise in both BIM technology and flood management | 3.73 | high | 1.223 | 3 |
| Resistance from individuals and organisations to adopt new BIM technologies in flood mitigation | 3.27 | moderate | 1.280 | 4 |
| Lack of standardisation in data exchange between different BIM software platforms in flood mitigation | 3.00 | moderate | 0.926 | 5 |
Based on Table 5, the “High initial investment costs for BIM implementation” was recorded as the most significant barrier with a mean score of 3.87, followed by the second-highest-ranked barrier, “limited access to accurate flood and terrain data” (mean = 3.73, SD = 0.961), indicating strong agreement among respondents on its impact. Interestingly, it is closely followed by the “lack of expertise in BIM and flood management”, also with an equal mean of 3.73 but a higher standard deviation (1.223), reflecting more varied responses. Despite having identical mean scores of 3.73, the greater consensus on data accessibility issues led to its higher ranking. Akmal (2022) also identified high investment costs in BIM software and hardware as the top challenge, especially for SMEs, further compounded by expenses for staff training or hiring skilled personnel.
Two organisational and technical issues recorded for moderately significant are “Resistance from individuals and organisations to adopt new BIM technologies in flood mitigation” (mean: 3.27) and “Lack of standardisation for data exchange” (mean: 3.00). It is noteworthy that “Resistance to change” showed the highest standard deviation (SD = 1.280), which suggests there might be a variation in opinions of respondents in the organisation for this specific barrier. Both of these barriers are also highlighted by Rahman et al. (2021) and Sa’adi et al. (2024), who noted that resistance to adopting BIM often comes from limited awareness and training, while the lack of data standardisation affects the collaboration and system integration. These issues continue to present high significance and consistent challenges in BIM implementation for flood mitigation.
These findings suggest that improving the adoption of BIM for flood mitigation requires a comprehensive approach. Key strategies should involve providing financial incentives to reduce entry barriers for firms, implementing specialised education and training programs to address the expertise gap, enhancing data accessibility and interoperability between software platforms, and fostering strong organisational leadership to drive the transition and effectively manage cultural resistance.
A reliability test was conducted using Cronbach’s alpha to evaluate the internal consistency of the Likert-scale items measuring the barriers to the implementation of BIM in flood mitigation. As shown in Table 6, the Cronbach’s alpha value obtained is 0.833, which indicates a good level of internal consistency among the 5 barrier items (Cronbach, 1951).
Cronbach’s alpha value of barriers to BIM implementation in flood mitigation (own research)
| Cronbach’s alpha value | Internal consistency |
|---|---|
| α = 0.833 | good |
This section provides a mean score analysis of strategies to enhance Building Information Modelling (BIM) adoption for flood mitigation within the case study organisation. Table 7 highlights key findings obtained from the respondents. The analysis aims to suggest stakeholders to overcome barriers to BIM implementation.
Mean score of strategies to enhance BIM adoption in flood mitigation (own research)
| Strategies to enhance BIM adoption in flood mitigation | Mean score | Mean score interpretation | Standard deviation | Rank |
|---|---|---|---|---|
| Promoting strong senior management support for BIM adoption in flood mitigation | 4.33 | high | 0.816 | 1 |
| Introducing the National BIM eSubmission (NBeS) system to facilitate building plan submissions and approvals, reducing turnaround time for flood mitigation projects | 4.00 | high | 1.195 | 2 |
| Providing comprehensive and affordable BIM training through the myBIM Centre for flood mitigation | 3.93 | high | 0.961 | 3 |
| Providing financial incentives for companies investing in BIM software, technology, and training for flood mitigation projects | 3.93 | high | 1.223 | 4 |
| Mandating the adoption of BIM for flood mitigation projects valued at RM10 million and above | 3.87 | high | 0.915 | 5 |
| Standardising workflows in flood mitigation projects by adopting ISO 19650, and using Common Data Environments for seamless collaboration | 3.87 | high | 0.915 | 5 |
Based on Table 7, the strategy “Promoting strong leadership support from senior management” recorded the highest mean score (4.33) with the lowest standard deviation (0.816), indicating a strong agreement on its importance. This suggests that adopting BIM for flood mitigation needs strong support from top management to succeed, as identified in the study by Rahman et al. (2021).
Furthermore, the second highly-rated strategy is “Introduction of the National BIM eSubmission (NBeS) system”, ranked second (Mean: 4.00), highlighting the value of streamlined processes for efficiency (CIDB, 2020). However, its higher standard deviation (1.195) suggests more varied views among the respondents.
Ranked third with a mean score of 3.93, the provision of affordable BIM training via platforms like the myBIM Centre introduced by CIDB (2020) addresses the identified skills gap, with consistent support reflected in a standard deviation of 0.961. It is closely followed by the strategy of offering financial incentives, which also scored 3.93 but had a higher standard deviation (1.223), indicating more varied opinions. This approach targets the high initial investment barrier and is generally well-received despite differing views.
Meanwhile, the strategies “Mandating BIM adoption” and “Standardising workflows by adopting ISO 19650” ranked equally in 5th place with the same mean score (3.87) and standard deviation (0.915). Although both are essential, their ranking suggests they are recognised as supportive measures that work alongside strong leadership and practical support. In summary, the analysis depicts a clear hierarchy of strategies for improving BIM adoption for flood mitigation. The primary driver is internal leadership, followed by practical process improvements, direct support to overcome skill and cost barriers, and finally, the establishment of a regulatory system.
A reliability test was conducted using Cronbach’s alpha to evaluate the internal consistency of the Likert-scale items measuring the strategies to enhance BIM adoption in flood mitigation. As shown in Table 8, the Cronbach’s alpha value obtained is 0.863, which indicates a good level of internal consistency among the 6 strategy items (Cronbach, 1951).
Cronbach’s alpha value of strategies to enhance BIM adoption in flood mitigation (own research)
| Cronbach’s alpha value | Internal consistency |
|---|---|
| α = 0.863 | good |
This study was conducted to explore strategies for improving the adoption of Building Information Modelling (BIM) in flood mitigation, focusing on a contractor organisation in Johor Bahru. The conclusion is structured according to the three research objectives set out in the study:
The study found that BIM adoption within the case study organisation is encouraging but not yet fully mature. A majority (66.7 %) of respondents reported frequent use of BIM, with 60 % operating at BIM Level 3, indicating a fair level of collaboration and data integration. However, a small portion of respondents still reported no usage of BIM, highlighting inconsistencies in adoption across departments or project types. This shows that while the organisation is on the right path, further alignment and internal standardisation are required.
Several key barriers were investigated through quantitative analysis. The most significant challenge reported was the high initial investment cost, followed by limited access to accurate flood and terrain data, and a lack of technical expertise in both BIM and flood-related modelling. These barriers were supported by a strong consensus among respondents, as indicated by low standard deviation scores in the survey results.
In response to the identified challenges, a series of strategic solutions was proposed and ranked by participants. The most effective strategy was the need for strong senior management support to drive internal BIM policies and investments. Other top-ranked strategies included the implementation of the National BIM e-Submission (NBeS) system to streamline regulatory compliance, provision of affordable training through platforms like myBIM Centre, offering financial incentives for technology investment, and mandating BIM usage for large-scale flood mitigation projects. Standardising workflows using ISO 19650 and integrating Common Data Environments (CDEs) were also recommended to ensure consistent data sharing and coordination.
In conclusion, the study highlights that BIM adoption for flood mitigation is progressing but faces both technical and organisational barriers in the case study organisation. Achieving full implementation requires a comprehensive approach involving leadership support, financial aid, capacity building, and data integration. With the right strategies in place, BIM can significantly enhance project efficiency, flood resilience, and contribute to sustainable urban development in Malaysia. This study also provides potentially valuable insights that could be relevant for enhancing flood mitigation and resilience in Johor Bahru’s construction industry.