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Building defects are becoming recurrent phenomena in most high-rise buildings. However, little research exists on the analysis of defects in high-rise buildings based on data from real-life projects. This study aims to develop dashboards and models for revealing the most common locations of defects, understanding associations among defects and predicting the rectification periods.

In total, 15,484 defect reports comprising qualitative and quantitative data were obtained from a company that provides consulting services for the construction industry in Victoria, Australia. Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations.

Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days.

The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. The stochastic models can provide a reliable point of reference for property managers, occupants and sub-contractors for taking measures to avoid reoccurring defects; so too, findings provide estimations of possible rectification periods for various types of defects.

This study aims to provide a framework for the interaction of blockchain technology, the audit process and International Financial Reporting Standards (IFRS).

This study was conducted in three phases. In the first phase, a meta-synthesis method was used until 2020. Out of 87 papers, a total number of 15 were selected, and 72 were excluded after an initial screening. In the second phase, 11 interviews were held with experts with sufficient experience and expertise in blockchain technology, accounting and auditing. These interviews were held between 2020 and 2021. The sampling was purposive (targeted) in type, along with snowball sampling. In the third phase, a combination of questionnaire-based method and the fuzzy Delphi method was used. The research population for this phase consisted of accounting professors, members of the Iranian Association of Certified Public Accountants and accounting and technology professionals. Following the selection of the Delphi panel, 35 questionnaires were collected between 2021 and 2022 for analysis.

The results showed that in line with the interaction of blockchain technology, audit process and IFRS, 52 indicators were confirmed in eight components. Moreover, “Verifiability,” “Timeliness” and “Predictive value” were the most influential factors, respectively, according to the conceptual model. In addition, higher average scores of experts’ views were related to “Transparency audit reports” and “Increasing the quality of the auditor’s judgment”, respectively.

Blockchain is a complex subject, and there has been very little published research on the intersection of IFRS and blockchain technology. It is crucial for the audit profession and technology professionals to focus on the interaction of blockchain technology with the qualitative characteristics of information. This is an area that has not been thoroughly researched yet. The interaction of blockchain technology, improvements in the audit process and adherence to IFRS is a significant development in accounting and auditing.