The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

In recent years, facility management (FM) has adopted many computer technology solutions for building maintenance, such as building information modelling (BIM) and computerized maintenance management systems (CMMS). However, maintenance requests management in buildings remains a manual and a time-consuming process that depends on human management. In this paper, a machine-learning algorithm based on natural language processing (NLP) is proposed to classify maintenance requests. This algorithm aims to assist the FM teams in managing day-to-day maintenance activities. A healthcare facility is addressed as a case study in this work. Ten-year maintenance records from the facility contributed to the design and development of the algorithm. Multiple NLP methods were used in this study, and the results reveal that the NLP model can classify work requests with an average accuracy of 78%. Furthermore, NLP methods have proven to be effective for managing unstructured text data.

This study presents a systematic literature review (SLR) of the Advanced Work Packaging (AWP) methodology, focusing on identifying barriers to its adoption. The review encompasses an analysis of 287 documents from 2013 to 2023, including conference articles, doctoral theses, journal articles, master theses, reports, and organizational documents. Following a rigorous selection process, 59 of these documents were identified as pertinent to the investigation. This research employs a dual approach. First, a scientometric analysis to map the collaborative networks of authors and the co-occurrence of keywords, providing a comprehensive picture of the research landscape in AWP. Second, the study delves into the main barriers hindering the adoption of AWP, as revealed through the SLR of the selected documents. The findings offer an overlay network visualization of coauthorship and a network visualization of keyword co-occurrence. The study culminates in a detailed identification of primary AWP barriers, suggestions for future research directions, and potential lines of inquiry within the field. This work contributes to the existing body of knowledge by offering a novel perspective on the challenges associated with AWP implementation and provides a foundation for future scholarly endeavors in this domain.

The demand on affording homes for the growing population in the world is noticeably increasing. This demand creates a high pressure to find solutions that can help in constructing quick and affordable shelters. In efforts to improve productivity, performance, and constructability of residential buildings, the direction toward modularity in construction is increasing. In order to gain the best benefits of modular systems in construction, these systems should be associated with project management practices. Among these practices, Advanced Work Packaging (AWP) is a construction driven planning tool that proved its value in improve the delivery and execution effectiveness in the oil and gas fields and currently is witnessing greater consideration in the construction industry. The current study aims to link the two concepts through proposing a framework to integrate AWP in residential modular construction. This article is also presenting an example about the possible way for this integration. The article also discusses the opportunity to improve productivity and installation costs due to the implementation of the proposed framework.

With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive measurements and models, such as admittance at the PCC. This classification poses a challenge as the TSO lacks precise knowledge of the actual control structures and algorithms. This paper introduces a novel classification algorithm based on Convolutional Neural Networks (CNN), capable of detecting patterns in sequential data. The proposed CNN utilizes a new architecture to separate admittances along the d and q axes, and a decision layer allows to determine the correct converter mode. The performance of the proposed CNN model was assessed through two tests and compared to the traditional feedforward model. The proposed CNN architecture demonstrates significant classification capabilities, as it is able to identify the control mode of the converter even when its control structure is not part of the training dataset.