This paper explores different building maintenance strategies in commercial buildings in Sydney, Australia, focusing on corrective maintenance (CM) and preventive maintenance (PM). While CM involves rectifying issues after they occur, PM aims to enhance productivity by anticipating potential issues. Although PM seems more logical, the decision to implement this type of maintenance strategy are typically made based on item reliability, failure frequency, and downtime cost, commonly found in manufacturing facilities or critical environments. However, as found in the selected/surveyed commercial real estate buildings, CM was more frequently adopted in aged facilities with older infrastructure, and PM was favoured for buildings without structural deficiencies; however, operating equipment failures were common. However, in many cases, decision makers did not consider the broader effects of downtime beyond direct financial losses, costs associated with customer satisfaction, worker efficiency, rent abatements, and reputation damage. While each building is unique and may require a bespoke maintenance schedule, this study’s insights may help managers select the most appropriate maintenance strategy. Nonetheless, further research is needed to investigate the role of innovative technologies (such as machine learning and artificial intelligence) in enhancing maintenance efficacy and explore the influences of economic shifts, corporate and financial objectives, and the availability of technical resources.

Structural design plays a very important role in reducing environmental impacts by reusing resources, recycling materials, and minimizing waste and pollution in the construction sector. Sustainable design becomes more effective than traditional solutions in achieving the transition to sustainability. The decision-making process is not simple due to the different preferences of clients, architects, and engineers. This paper aims to develop a decision framework for assessing sustainability in the early structural design stage. Multi-criteria decision-aiding (MCDA) methods have been implemented to improve the selection of regulations. A technical ranking approach, the Fuzzy Analytic Hierarchy Process (FAHP) method, has been employed to identify the optimal solution. Three alternatives including an innovative and two traditional structural systems have been selected and compared in terms of three criteria—economic, social, and environmental impacts. Nine sub-criteria for ranking the importance level of sustainable design have been determined through a literature review and professional experts. FAHP methods show that the economic impact (58%) is the most important criterion for assessing the sustainability of structural systems, followed by the environment with 31%. The social aspect contributes 11% to this method, and it is ranked as the least important criterion. This research revealed that MCDA methods can be used as a guideline for engineers to improve the selection in the process of sustainable design. The decision model proposed in this study has been verified and, therefore, can be applied for similar projects.

High-speed railway is trending in developing countries for economic reasons, mobility in the aftermath of COVID-19, and environmental concerns. The high-speed railway operators continuously improve the operational speed to transport more passengers in less time. However, increasing the train loads at high speed might increase the dynamic loads of bridges and affect their pile foundation. A stiffer railway bridge is mandatory for high-speed train safety and passenger riding comfort. However, a flexible bridge is ideal for responding to earthquakes. Thus, these two objectives are conflicting. This review paper provides a bibliometric review aiming to determine the published studies by year and by country, and to visualize different research trends in cluster maps using the VOSviewer software, summarizing the published research for high-speed railway bridges starting from 1964. The review also extracted information from the latest studies by summarizing some essential objectives, useful methodologies, and notable findings that might be applicable to future studies. In conclusion, there is a need for further research to fill the knowledge gap in the study related to the soil–structure interaction phenomenon considering the performance-based seismic design of a high-speed railway bridge on a monopile foundation in the event of lateral spreading due to soil liquefaction.

The maintenance of critical maritime infrastructure is essential for ensuring the safe, reliable, and efficient operations of marine seaports. This paper proposes a novel fuzzy multi-criteria decision framework for evaluating the maintenance practices and culture of maintenance-critical maritime infrastructure, such as port loading and unloading machinery and equipment. The proposed framework incorporates three distinct multi-criteria decision-making tools Step-wise Weight Assessment Ratio Analysis, Weighted Aggregate Sum Product Assessment, and Technique for Order of Preference by Similarity to Ideal Solution. Fuzzy logic is incorporated into the framework to enhance the precision and robustness of the evaluation process. To form the basis of the assessment, the framework is structured around five key maintenance practice criteria: planning and scheduling; data collection and analysis; documentation and record keeping; maintenance personnel training; and competency, and four important maintenance culture criteria: leadership commitment, proactive and preventive approach, safety and compliance focus, and continuous improvement and learning. To validate the framework, an empirical evaluation was conducted, analyzing maintenance practices and culture across six Nigerian seaports. Data collection uses a questionnaire administered to relevant maintenance experts in the ports, ensuring a comprehensive and expert-informed analysis. The data collected was then analyzed using the fuzzy multi-criteria decision framework. The results provide valuable and actionable insights into the current maintenance practices and maintenance culture of the ports, identifying areas for improvement.

The Building Condition Index (BCI) is a widely adopted quantitative metric for assessing various aspects of a building’s condition, as it facilitates decision-making regarding maintenance, capital improvements and, most importantly, the identification of investment risk. In practice, longitudinal BCI scores are typically used to identify maintenance liabilities and trends and proactively provide indications when maintenance strategies need to be altered. This allows for a more efficient resource allocation and helps maximise the lifespan and functionality of buildings and their assets. Given the historical ambiguity concerns because of the reliance on visual inspections, this research investigates how AI and using ANN, DNN and CNN can improve the predictive accuracy of determining a recognisable Building Condition Index. It demonstrates how ANN and DNN perform over asset classes (apartment complexes, education and commercial buildings). The results suggest that DNN architecture is adept at dealing with diverse and complex datasets, thus enabling a more versatile BCI prediction model over various building categories. It is envisaged that with the expansion and maturity of ANN, DNN and CNN, the BCI calculation methodologies will become more sophisticated, automated and integrated with traditional assessment approaches.