The architecture, engineering, and construction (AEC) industry has seen a significant rise in the adoption of Building Information Modeling (BIM) in the last few years. BIM software have launched with numerous robust capabilities and features to satisfy the ever-demanding needs of the AEC industry. Various factors are associated with the selection of BIM software depending on a company’s requirements and constraints. BIM software selection is a daunting process as most AEC industries are unaware of the factors to consider when making this important decision. This study focuses on identifying the critical success factors (CSFs) and their interrelationship for efficient BIM software selection. For this research, a questionnaire was developed and disseminated in two stages in India, the United States of America (U.S.A.), Germany, and the United Kingdom (U.K.). In the first stage, a total of twenty-six identified CSFs were analyzed with the factor comparison method (FCM) to identify the top fifteen CSFs. Subsequently, the identified top fifteen CSFs were further assessed by implementing Fuzzy DEMATEL to categorize them into cause-and-effect groups based on respective influence strength, depicted with a causal diagram. Out of fifteen CSFs, five and ten factors were grouped into the cause group and effect group for BIM software selection, respectively. The most important factors were identified as software functionality, BIM adoption strategies and processes, interoperability, staff competencies, BIM standards and regional regulations. The outcome of this research can help BIM user companies improve their BIM software selection framework and decision-making process during purchasing software.

Large and complex projects have become commonplace, increasing the need to engage contractors in pre-construction services (PCS). Clients now have a range of procurement options that can involve a contractor in any phase of the pre-construction process. This research investigated what factors are important when deciding how soon to engage the contractor. Furthermore, it investigated the issues of engaging the contractor too early or too late. This study collected results through structured explorative interviews with senior staff from client, consultant, and contractor organizations in New Zealand. The results are presented, compared, and discussed for each respective viewpoint. The results show that cost (scale) and complexity of the project, the reputation of the involved parties, and the nature of the relationships are the most crucial factors. However, the parties differ on the best timing for PCS. The contractor argued that PCS should occur in the concept design phase. The client opined that for some projects they would prefer involvement by the contractor in the project definition phase, while for others this should occur later, in the detailed design phase. Consultants recommended that the contractor should be included in the later phases before construction commences. It is interesting to note that the contractor does not always want to be involved early. Generally, the contractor does not want to be involved when the award of the project is still uncertain. Finally, it became apparent that a significant number of issues stem from the contractual arrangements of PCS, which poorly dictates who controls the design process.

Pavement surface deflection has been used by researchers and highway agencies to assess the structural condition of the pavement structure. None of the currently available approaches provides an acceptable evaluation method for the rigid pavement structural capacity at the network level. In this research, pavement structural ratio (PSR) and overall pavement structural index (OPSI) were derived from deflection bowls generated from finite element simulations and validated by actual field deflection data measured by falling weight deflectometer and performance data extracted from long-term pavement performance database. The PSR parameter provides structural evaluation of the rigid pavement slab and the base course above the subgrade only. Whereas, OPSI parameter provides an overall evaluation of the pavement structure and the subgrade.

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With the advancement of digital technology, the collection of pavement performance data has become commonplace. The improvement of tools to extract useful information from pavement databases has become a priority to justify expenditures. This paper presents a case study of PaveMD, a tool that integrates multi-dimensional data structures with a data-driven fuzzy approach to identify good performing pavement sections. Combining this tool with an innovative paradigm where the focus is on repeating success can bring additional value to existing pavement databases. The case study shows that PaveMD can identify pavement sections that are performing well by comparing performance measures for the New Zealand context. In this paper, PaveMD's development is described, and its implementation is showcased using data from the New Zealand Long-Term Pavement Performance (LTPP) database. It is recommended that this approach be further developed and extended to other infrastructure databases internationally.