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This paper models the deterioration process of a multi-component system. Each deterioration process is modelled by the Wiener process. The purposes of this paper are to address these issues and consider the cost process based on the multi-component system.

Condition-based Maintenance is a method for reducing the probability of system failures as well as the operating cost. Nowadays, a system is composed of multiple components. If the deteriorating process of each component can be monitored and then modelled by a stochastic process, the deteriorating process of the system is a stochastic process. The cost of repairing failures of the components in the system forms a stochastic process as well and is known as a cost process.

When a linear combination of the processes, which can be the deterioration processes and the cost processes, exceeds a pre-specified threshold, a replacement policy will be carried out to preventively maintain the system.

Under this setting, this paper investigates maintenance policies based on the deterioration process and the cost process. Numerical examples are given to illustrate the optimisation process.

Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London.

This study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)).

GB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework.

Industry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

Operation and maintenance (O&M) processes projects such as identification, assessment, planning and execution, embody a variety of standards such as technical (method of statement), environmental, economic (campus development) and social (health and wellbeing). Because these standards have proven to be challenging to integrate, local governments are increasingly experimenting with social innovation (SI) as a bottom-up form of standard integration. This study aims to apply the concept of SI to the O&M processes of facilities management at polytechnics in Malaysia to identify problems with conventional working practices in this area and to recommend potential solutions.

The paper reviews evidence that conventional working methods generate significant problems related to paper-based forms, improper database management and flawed decision-making processes. Because of the lack knowledge about different ways of how standard integration is achieved, the comparison of three polytechnic institutions which are Rensselaer Polytechnic Institute (RPI) and Southern Polytechnic College of Engineering and Engineering Technology (SPCEET) in USA as well as Seberang Perai Polytechnic, Pulau Pinang (PSP) in Malaysia shares the ambition to realise standard integration of O&M through SI.

The findings reveal that SI leads to four ways of standard integration: computerised maintenance management system, online customer complaint, electronic form and relational database. Application of the concept of SI reveals the need for more sophisticated management solutions in the O&M processes of facilities management.

These standard integration arrangements unfortunately seem to mainly contribute to greater alignment between standard rather than true standard integration. The concept of SI will guide future improvements and developments in maintenance management systems to fulfil requirements in this area.

Current research efforts exhibit a surge imperative for a building information modelling (BIM) approach that embodies a repository of all relevant data of existing building components while monitoring and consistently recording numerous components’ functions throughout its lifecycle, especially in Egypt. This research paper aims to develop an integrated as-is BIM-facility management (FM) information model for the existing building’s components via a case study, depicting a repository for historical data and knowledge amassed from inspections and conveying maintenance decisions automatically during the FM practices.

The developed approach pursues four successive steps: data acquisition and processing of building components; components recognition from point clouds; modelling scanned point clouds; and quick response code information transfer to BIM components.

The proposed approach incorporates the as-is BIM with the building components’ as-is FM information to portray a repository for historical data and knowledge collected from inspections to proactively benefit facility managers in simplifying, expediting and enhancing maintenance decisions automatically during FM practices.

This paper presents a digital alternative to manual maintenance recordkeeping concerning building components to retrieve their as-is and historical data using a case study in Egypt. This paper proposes a broad scan to as-is information BIM approach for the existing building’s components to condone maintenance interventions using a versatile, affordable, readily available and multi-functional method for scanning the building’s components using a handheld tool.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

The construction industry in Nigeria, like many others worldwide, grapples with increasing demands for enhanced efficiency, transparency and security. While distributed ledger technology (DLT) offers substantial potential in tackling these issues, its implementation faces numerous obstacles. This study aims to investigate the challenges hindering the adoption of DLT within the Nigerian construction sector, capitalizing on a recognized research gap in this field.

A comprehensive literature review identified 20 challenges to DLT adoption. A structured questionnaire was developed and distributed to 98 construction professionals selected from a pool of 5,330 members using purposive and snowball sampling. Background information was assessed using frequency and percentage, data normality with the Shapiro–Wilk test and nonparametric data compared using the Kruskal–Wallis H-test. Factor analysis was then used to reduce the challenges into a more coherent set.

Findings revealed the top five challenges: power supply issues, data privacy and compliance, an uncertain legal framework, insufficient training and education and a lack of skilled workforce. Further analysis revealed four clusters of challenges, namely, technical and infrastructure barriers, organizational and cultural challenges, financial and economic challenges and regulatory and legal challenges.

Through the evaluation of challenges and potential strategies to overcome them, this study aims to lay a foundation that assists construction entities in effectively harnessing the benefits of DLT while navigating the obstacles inherent in its adoption.

This study contributes to the existing body of knowledge by offering insights into the challenges surrounding the adoption of DLT, specifically within the Nigerian construction sector. By juxtaposing the findings from this study with existing research conducted in other countries such as India, South Africa and the USA, a more comprehensive understanding of the challenges unique to the Nigerian context can be gained.

Management of power distribution companies (discoms) in India has been historically criticized on the ground of inefficient management. Inefficiency in operations triggered management by private franchisees for promotion of managerial and technical expertise. However, franchise contracts have achieved mixed outcomes despite the business model being a decade old in the Indian power distribution sector. Therefore, this study sheds light on the drivers of discoms (principal) with the franchisees (agent) for the achievement of the common performance goals, highlighting the agency issues at multiple levels across the organizational hierarchies. The study seeks to acknowledge the commonalities and differences between and across varying levels.

A qualitative embedded single case study was conducted in an Indian state, namely Odisha. The study was built on archival analysis, personal observations and semi-structured interviews with the franchisors and franchisee officials across the organization's hierarchical levels. A conceptual model based on the review of prior literature formed the set of coding and presentation for the study.

The study provides insights on factors that play a role in effective power distribution management, operational efficiency and improved financial performance through the partnership of the principal and the agent.

The study is predominantly dependent upon interviews. This paved the way for the limitation of human biases. Additionally, deep insights were drawn from a single case study of a discom's decision to hire franchisees. However, this was at the cost of the number of organizations interviewed. The findings of the study could be built across other areas or nations.

There is adequate literature on franchising as a business model. However, literature is lacking in highlighting the commonalities and differences between different contracting parties and their impact on the performance of the contract. Additionally, there is a dearth of literature on franchising in the power distribution sector. Therefore, studying the model from multiple perspectives would contribute to the literature on the power sector and franchising.

This study investigates the role of supply chain knowledge management in enhancing pharmaceutical supply chain resilience.

This study focusses on the Middle East region, where semi-structured online interviews were conducted with 38 professionals from the pharmaceutical supply chain to collect empirical data.

The study reveals that supply chain knowledge management is a crucial value-adding practice that improves pharmaceutical supply chain resilience. Effective supply chain knowledge management enables organisations to develop agility, change, adaptability, problem-solving, response and innovation capabilities that support supply chain resilience. However, challenges related to supply chain management practices, people, processes and technology hinder the effective promotion of supply chain knowledge.

This study reminds managers that knowledge management is critical for building resilience in supply chains.

The study highlights the importance of a resilient pharmaceutical supply chain for organisations and society. The study advocates that effective supply chain knowledge management can help ensure a sustained supply of high-quality pharmaceutical products and services during crises.

The study offers novel insights by examining pharmaceutical supply chain resilience from a knowledge management perspective and highlighting the potential of knowledge capabilities to enable supply chains to recover from crises and adapt to the new normal. This study also highlights the key strategic considerations for managing knowledge effectively throughout the supply chain.

Our study examines how artificial intelligence (AI) can enhance decision-making processes to promote circular economy practices within the utility sector.

A unique case study of Alia Servizi Ambientali Spa, an Italian multi-utility company using AI for waste management, is analyzed using the Gioia method and semi-structured interviews.

Our study discovers the proactive role of the user in waste management processes, the importance of economic incentives to increase the usefulness of the technology and the role of AI in waste management transformation processes (e.g. glass waste).

The present study enhances the circular economy model (transformation, distribution and recovery), uncovering AI’s role in waste management. Finally, we inspire managers with algorithms used for data-driven decisions.