In terms of sustainable growth, Malaysia’s construction industry faces significant challenges, such as construction costs and lack of awareness. To meet these challenges, it is essential to integrate building information modeling (BIM) and sustainable development. As a result, this study aimed to establish strategies for adopting BIM in sustainable building projects. A systematic literature review was performed to classify the strategies to accomplish this aim, followed by a questionnaire survey of 129 construction project stakeholders. Mean score and exploratory factor analysis (EFA) were conducted to explore the detailed influence of BIM adoption strategies in sustainable building projects. The results revealed that “workshops, lectures, and conference events are used to enhance public awareness” and “better information on the costs and benefits of sustainable materials” and “strengthened sustainable development” were the most essential strategies to boost sustainable growth in building projects. Furthermore, EFA was conducted to undertake the group analysis, namely, to identify the standardization-related strategy, economic-related strategy, awareness-related strategy, and environment-related strategy. This study paves the way for future scholars, policymakers, and practitioners to investigate the limitations of future studies.

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.