Search results

Many buildings fail to meet user expectations, causing a performance gap. Pre-occupancy evaluation (PrOE) is believed to have the potential to close the gap. It enables designers to obtain end-user feedback in the design phase and improve the design for better performance. However, PrOE implementation faces challenges due to still maturing knowledgebase. This study aims to understand the state-of-the-art knowledge of PrOE, thereby identifying future research needs to advance the domain.

A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework was conducted. A thorough search in five databases and Google Scholar retrieved 90 articles, with 30 selected for systematic review after eliminating duplicates and irrelevant articles. Bibliometric analyses were performed using VOSviewer and Biblioshiny on the article metadata, and thematic analyses were conducted on their contents.

PrOE is a vehicle for engaging building end-users in the design phase to address the credibility gap caused by the discrepancies between the expected and actual performance of buildings. PrOE has gained limited applications in healthcare, residential, office and educational building design for two broad purposes: design management and marketing. Using virtual reality technologies for PrOE has demonstrated significant benefits. Yet, the PrOE domain needs to mature in multiple perspectives to serve its intended purpose effectively.

This study identifies four knowledge gaps for future research to advance the PrOE domain: (1) developing a holistic PrOE framework, integrating comprehensive performance evaluation criteria, useable at different stages of the design phase and multi-criteria decision algorithms, (2) developing a mixed reality tool, embodying the holistic PrOE framework, (3) formulating a PrOE framework for adaptive reuse of buildings and (4) managing uncertainties in user requirements during the lifecycle in PrOE decisions.

Master information delivery plan (MIDP) is a key requirement for building information modelling (BIM) execution plan (BEP) that enlists all information deliverables in BIM-based project, containing information about what would be prepared, when, by who, as well as the procedures and protocols to be used. In a well-conceived BEP, the MIDP facilitates collaboration among stakeholders. However, current approaches to generating MIDP are manual, making it tedious, error-prone and inconsistent, thereby limiting some expected benefits of BIM implementation. The purpose of this study is to automate the MIDP and demonstrate a collaborative BIM system that overcomes the problems associated with the traditional approach.

A BIM cloud-based system (named Auto-BIMApp) involving naming that automated MIDP generation is presented. A participatory action research methodology involving academia and industry stakeholders is followed to design and validate the Auto-BIMApp.

A mixed-method experiment is conducted to compare the proposed automated generation of MIDP using Auto-BIMApp with the traditional practice of using spreadsheets. The quantitative results show over 500% increased work efficiency, with improved and error-free collaboration among team members through Auto-BIMApp. Moreover, the responses from the participants using Auto-BIMApp during the experiment shows positive feedback in term of ease of use and automated functionalities of the Auto-BIMApp.

The replacement of traditional practices to a complete automated collaborative system for the generation of MIDP, with substantial productivity improvement, brings novelty to the present research. The Auto-BIMApp involve multidimensional information, multiple platforms, multiple types and levels of users, and generates three different representations of MIDP.

This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process specific to the Al-357 alloy.

Validation of a 3D heat transfer simulation model was conducted to forecast melt pool dimensions, involving variations in laser power, laser scanning speed, powder bed thickness (PBT) and powder bed pre-heating (PHB). Using the validated model, a data set was compiled to establish a back-propagation-based machine learning capable of predicting melt pool dimensional ratios indicative of printing defects.

The study revealed that, apart from process parameters, PBT and PHB significantly influenced defect formation. Elevated PHBs were identified as contributors to increased lack of fusion and keyhole defects. Optimal combinations were pinpointed, such as 30.0 µm PBT with 90.0 and 120.0 °C PHBs and 50.0 µm PBT with 120.0 °C PHB.

The integrated process mapping approach showcased the potential to expedite the qualification of LPBF parameters for Al-357 alloy by minimizing the need for iterative physical testing.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

This paper aims to use hybrid manufacturing (HM) to overcome several drawbacks of material extrusion three-dimensional (3D) printers, such as low dimension ranging from 0.2 to 0.5 µm, resulting in a noticeable staircase effect and elevated surface roughness.

Subtractive manufacturing (SM) through computer numerical control milling is renowned for its precision and superior surface finish. This study integrates additive manufacturing (AM) and SM into a single material extrusion 3D printer platform, creating a HM system. Two sets of specimens, one exclusively printed and the other subjected to both printing and milling, were assessed for dimension accuracy and surface roughness.

The outcomes were promising, with postmilling accuracy reaching 99.94%. Significant reductions in surface roughness were observed at 90° (93.4% decrease from 15.598 to 1.030 µm), 45° (89% decrease from 26.727 to 2.946 µm) and the face plane (71% decrease from 12.176 to 3.535 µm).

The 3D printer was custom-built based on material extrusion and modified with an additional milling tool on the same gantry. An economic evaluation based on cost-manufacturing demonstrated that constructing this dual-function 3D printer costs less than US$560 in materials, offering valuable insights for researchers looking to replicate a similar machine.

The modified general 3D printer platform offered an easy way to postprocessing without removing the workpiece from the bed. This mechanism can reduce the downtime of changing the machine. The proven increased dimension accuracy and reduced surface roughness value increase the value of 3D-printed specimens.

Additive manufacturing (AM) is growing economically because of its cost-effective design flexibility. However, it faces challenges such as interlaminar weaknesses and reduced strength because of product anisotropy. Therefore, the purpose of this study is to develop a methodology that integrates design for additive manufacturing (AM) principles with fused filament fabrication (FFF) to address these challenges, thereby enhancing product reliability and strength.

Developed through case analysis and literature review, this methodology focuses on design methodology for AM (DFAM) principles applied to FFF for high mechanical performance applications. A DFAM database is constructed to identify common requirements and establish design rules, validated through a case study.

Existing DFAM approaches often lack failure theory integration, especially in FFF, emphasizing mechanical characterizations over predictive failure analysis in functional parts. This methodology addresses this gap by enhancing product reliability through failure prediction in high-performance FFF applications.

While some DFAM methods exist for high-performance FFF, they are often specific cases. Existing DFAM methodologies typically apply broadly across AM processes without a specific focus on failure theories in functional parts. This methodology integrates FFF with a failure theory approach to strengthen product reliability in high-performance applications.

Drawing on a dynamic capability view, this study develops a decision support model that determines the most suitable configuration of strategies and challenges to adopt additive manufacturing (AM) to expedite digital transformation and performance improvement of the surgical and medical device (SMD) supply chain.

To investigate the research objective, a multi-method and multi-study research design was deployed using quality function deployment and fuzzy set qualitative comparative analysis.

The study finds that only resilience strategies or negation (i.e. minimisation) of challenges are not enough; instead, a configuration of resilience strategies and negation of challenges is highly significant in enhancing performance.

SMD supply chain decision-makers will find the decision support model presented in this study as beneficial to be resilient against various challenges in the digital transformation of service delivery process.

This study builds new knowledge of the adoption of AM technology in the SMD supply chain. The decision support model developed in this study is unique and highly effective for fostering digital transformation and enhancing SMD supply chain performance.

This study aims to assess the usability of augmented reality (AR) based mobile app for excavation and earthmoving processes using a novel tool entitled Excavator Augmented Reality (EAR).

A mixed-methods research approach was used through conducting experimentation to collect qualitative and quantitative data collected from the Sri Lankan construction sector. EAR app was used for experimentation in outdoor areas examining how a 360° tracked hydraulic excavator can be navigated in different physical environments similar to the real prospected job.

The findings reveal that EAR could make a considerable impact on enhancing productivity, safety and training processes. However, the developed EAR App subjected to assessment demonstrated the highest satisfaction gap for the auditory aspects. Among the remaining criterion, the satisfaction met user expectations for comfortability and no-risk practice. An analysis of strengths, weaknesses, opportunities and threats (SWOT analysis) conducted revealed that visualising the excavator activities and the requirements of improved features were the highest agreed strengths and weaknesses of the EAR. Among the opportunities for improvement, the necessity of improving emergency and safety reached the highest agreement. Moreover, the study presented the challenges in introducing mobile augmented reality (MAR) to the construction sector under the political, economic, sociocultural, technological, environmental and legal (PESTEL) model along with solutions to be taken.

This study provides a novel approach to addressing the safety, productivity and training concerns in heavy mobile plants and machinery on construction sites which remains to be unexplored to this end.

This study proposes an assessment framework for improving smart building performance in the broader context of smart city development, considering dimensions like environmental sustainability, building characteristics, intelligence, computation management and analytics. The framework is crafted to guide future research, aligning with the growing emphasis on sustainability and intelligence in evolving urban landscapes within smart cities.

In the initial phase, the concepts of “Smart City” and “Smart Buildings” are analyzed through a systematic literature review, considering the impact of governance on city sustainability and growth, along with the role of public policies in transforming buildings and cities. The empirical research evaluates innovation levels in small and medium-sized European cities, proposing a new framework with validated dimensions and sub-dimensions. This validation involves input from international experts through a Focus Group.

The key research findings validate the new proposed assessment framework for smart buildings within smart city development. The experts’ insights align with and support the dimensions identified in the bibliographic research, providing a comprehensive understanding of the role of smart buildings in sustainable urban development.

This framework not only provides insights for a new model with specific dimensions and sub-dimensions but also serves as a guide for formulating strategies and policies to enhance innovation in these settings. The value of this approach is strengthened by the validation and consolidation process involving international experts in the field.

As Industry 4.0 revolutionizes workplaces with unprecedented technological advancements, this chapter underscores the paramount importance of prioritizing human well-being and engagement. It navigates through a comprehensive array of strategies and practices that empower organizations to forge a work environment that is not only technologically advanced but also profoundly supportive, gratifying and motivating for employees (Froschauer et al., 2021). By elucidating how organizations can empower employees with autonomy while fostering collaborative endeavours, it uncovers a pathway to empowerment and job satisfaction (Caldarola et al., 2019; Kadir & Broberg, 2021). This chapter illustrates how organizations can harness these technologies to provide tailored growth experiences, thereby contributing to a thriving workforce. Navigating the ethical landscape of the digital workplace, this chapter examines the profound implications of Industry 4.0 on employee well-being. Delving into issues of privacy, transparency and equitable treatment, it imparts essential considerations for organizations seeking to align their practices with ethical imperatives. The methodology will incorporate case studies specific to the UAE market, providing a localized lens through which to analyze and implement human-centred workplace strategies (Mütze-Niewöhner et al., 2022; Urrutia Pereira et al., 2022). This chapter presents a holistic guide for organizations seeking to infuse human-centred principles into their Industry 4.0 workplaces (Caldarola et al., 2019; Longo et al., 2022). By championing well-being, job satisfaction and fulfilment, it equips leaders and decision-makers with actionable strategies to cultivate a work culture that thrives amid the rapid march of technological progress (Aromaa et al., 2019; Froschauer et al., 2021).