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The perception that the repetitive nature and attributes of mass housing projects (MHPs) induce significant influence on communication among projects teams have persistently been acknowledged without an empirical accentuation. This seemingly untested knowledge tends to limit the predictive accuracy of success and effectiveness of adopted communication style, strategies and models in mass housing particularly due to the incidence of the repetitive attributes. The purpose of this study is to delineate the influence of the repetitive attributes of mass housing projects on communication performance among the project team.

Through the use of questionnaire survey and structural equation modelling analysis, a hypothesized model tested evaluated the effects of the repetitive attributes of mass housing on information flow and information composition communication performance.

In the case of influence on information flow, it was seen to be substantial whereas that of the information composition was moderate.

The findings offer empirical credence to the existing perception and indeed affirm that the repetitive features of MHPs significantly contribute to communication performance related to information flow and information composition among the project team. The implication of these findings is that, practitioners and stakeholders on mass housing are urged to explore bespoke communication methods, medium, strategies and management approaches that fit the MHP attributes and environment to engender managerial and communication efficiencies in the delivery.

Industrialized construction (IC) is promoted to address some of the ills associated with the processes and products of the traditional construction approach. With several successful projects, IC is progressively becoming a preferred alternative construction approach and spurred the interest of contractors, developers and housing authorities in the technology. Increasingly, these stakeholders are keen to ascertain the compatibility and feasibility of using IC in their projects. This paper aims to develop a knowledge-based decision support framework for implementing industrialized construction projects (ICPs) that can facilitate better and informed decision-making when deciding to implement ICPs.

A comprehensive literature review was implemented to recruit 40 decision support factors (DSFs) and grouped into project requirements, location and site attribute, labour considerations and organizational factors. A 3-member expert panel validated the relevance of 35 DSFs, which became candidates for a structured questionnaire survey of experts in 18 countries. Statistical techniques are used to evaluate and prioritize the DSFs, leading to the development of a conceptual framework.

Statistical analysis revealed 33 significant DSFs. The top five most significant factors that could influence the decision to implement IC in a project include a stringent requirement for project quality control, suitability of the design for IC, organizational readiness and competencies in ICPs, client receptivity to IC and the need to minimize field construction time. A framework of project requirements, location and site attributes, labour considerations and organizational factors was proposed as decision support.

The proposed framework may help to inform decision-making regarding the implementation of IC in a project. It has wider applicability because it includes technical, managerial and operational aspects of and the required competencies for IC, which are shared between project types and territories. The prioritized DSFs could be used as a guide when implementing IC, especially in countries where bespoke decision support systems cannot be developed.

The paper delineated the most important DSFs that are shared between IC project types and territories and can be used to investigate the compatibility of using IC in a proposed project. This research constitutes the first exclusive attempt at delineating, quantifying and ranking the sets of decision-making factors, drawing on international data set and contributes to the empirical checklist of DSFs for ICPs.

Modular integrated construction (MiC) is considered as a process innovation to improve the performance of construction projects. However, effective delivery of MiC projects requires management of risks and uncertainties throughout its delivery chain. Although the design stage of MiC projects is usually managed with limited knowledge based on highly uncertain data and associated with epistemic uncertainties, MiC design risks have not received adequate research attention relative to other stages. The purpose of this paper is to conduct a knowledge-based evaluation and ranking of the design risk factors (DRFs) for MiC projects.

The paper reviewed the relevant literature to identify potential DRFs and validated their relevance through pilot expert review. The paper then used questionnaires to gather data from international MiC experts from 18 countries and statistically analyzed the data set.

Analysis results showed that the five most significant DRFs for MiC projects include unsuitability of design for the MiC method; late involvement of suppliers, fabricators and contractors; inaccurate information, defective design and change order; design information gap between the designer and fabricator; and lack of bespoke MiC design codes and guidelines. A correlation analysis showed that majority of the DRFs have statistically significant positive relationships and could inform practitioners on the dynamic links between the DRFs.

The paper provides useful insight and knowledge to MiC practitioners and researchers on the risk factors that could compromise the success of MiC project designs and may inform design risk management. The dynamic linkages among the DRFs instruct the need to adopt a system-thinking philosophy in MiC project design.

This paper presents the first study that specifically evaluates and prioritizes the risk events at the design stage of MiC projects. It sets forth recommendations for addressing the identified DRFs for MiC projects.

Corrosion loop development is an integral part of the risk-based inspection (RBI) methodology. The corrosion loop approach allows a group of piping to be analyzed simultaneously, thus reducing non-value adding activities by eliminating repetitive degradation mechanism assessment for piping with similar operational and design characteristics. However, the development of the corrosion loop requires rigorous process that involves a considerable amount of engineering man-hours. Moreover, corrosion loop development process is a type of knowledge-intensive work that involves engineering judgement and intuition, causing the output to have high variability. The purpose of this paper is to reduce the amount of time and output variability of corrosion loop development process by utilizing machine learning and group technology method.

To achieve the research objectives, k-means clustering and non-hierarchical classification model are utilized to construct an algorithm that allows automation and a more effective and efficient corrosion loop development process. A case study is provided to demonstrate the functionality and performance of the corrosion loop development algorithm on an actual piping data set.

The results show that corrosion loops generated by the algorithm have lower variability and higher coherence than corrosion loops produced by manual work. Additionally, the utilization of the algorithm simplifies the corrosion loop development workflow, which potentially reduces the amount of time required to complete the development. The application of corrosion loop development algorithm is expected to generate a “leaner” overall RBI assessment process.

Although the algorithm allows a part of corrosion loop development workflow to be automated, it is still deemed as necessary to allow the incorporation of the engineer’s expertise, experience and intuition into the algorithm outputs in order to capture tacit knowledge and refine insights generated by the algorithm intelligence.

This study shows that the advancement of Big Data analytics and artificial intelligence can promote the substitution of machines for human labors to conduct highly complex tasks requiring high qualifications and cognitive skills, including inspection and maintenance management area.

This paper discusses the novel way of developing a corrosion loop. The development of corrosion loop is an integral part of the RBI methodology, but it has less attention among scholars in inspection and maintenance-related subjects.

Weight loss services feature high consumer involvement that is sometimes marked by repetitive failures. These features can affect regret and its associated factors differently from the way that discrete failure can. The purpose of this study is to investigate consumer regret over repetitive failures in weight loss services as well as its antecedents (overeating and insufficient exercise), consequences (rumination and reflection) and moderators (failure experiences and required effort). This study also investigates how rumination and reflection affect persistence intention.

A total of 318 samples were collected through three surveys, one of which contained a scenario, provided to consumers who experienced repetitive failures in weight loss. The proposed relationships were tested using structural equation modeling, dominance analysis and PROCESS modeling.

The results of this study reveal that overeating contributes to regret more saliently than does insufficient exercise. The effect of regret on rumination (thoughts about continuing to blame oneself and giving up the pursuit of goals) is stronger than on reflection (thoughts about learning from prior failures and willingness to try again), and greater reflection results in higher persistence intention. Moreover, the effect of insufficient exercise on regret and the effect of regret on rumination are augmented with cumulative failure experiences, whereas required effort enhances the impact of regret on reflection.

This study is the first to focus on regret over repetitive failures in weight loss. It advances the literature by clarifying the antecedents and consequences of regret, showing how failure experiences influence the relationships between regret and its associated factors as well as identifying interventions that benefit from regret.

A common pipeline of apparel design and simulation is adjusting 2D apparel patterns, putting them onto a virtual human model and performing 3D physically based simulation. However, manually adjusting 2D apparel patterns and performing simulations require repetitive adjustments and trials in order to achieve satisfactory results. To support future made-to-fit apparel design and manufacturing, efficient tools for fast custom design purposes are desired. The purpose of this paper is to propose a method to automatically adjust 2D apparel patterns and rapidly generate acustom apparel style for a given human model.

The authors first pre-define a set of constraints using feature points, feature lines and ease allowance for existing apparels and human models. The authors formulate the apparel fitting to a human model, as a process of optimization using these predefined constraints. Then, the authors iteratively solve the problem by minimizing the total fitting metric.

The authors observed that through reusing existing apparel styles, the process of designing apparels can be greatly simplified. The authors used a new fitting function to measure the geometric fitting of corresponding feature points/lines between apparels and a human model. Then, the optimized 2D patterns are automatically obtained by minimizing the matching function. The authors’ experiments show that the authors’ approach can increase the reusability of existing apparel styles and improve apparel design efficiency.

There are some limitations. First, in order to achieve interactive performance, the authors’ current 3D simulation does not detect collision within or between adjacent apparel surfaces. Second, the authors’ did not consider multiple layer apparels. It is non-trivial to define ease allowance between multiple layers.

The authors use a set of constraints such as ease allowance, feature points, feature lines, etc. for existing apparels and human models. The authors define a few new fitting functions using these pre-specified constraints. During physics-driven simulation, the authors iteratively minimize these fitting functions.

This paper aims at improving progress tracking and control of repetitive projects by developing a novel framework that automates the documentation of as-built information directly into the project schedule and also introduces enhanced linear scheduling formulation to support project control decisions.

The proposed framework uses e-mail technology to facilitate detailed tracking of daily as-built events of all parties through bidirectional communication between site and head office. It also provides a new formulation for more accurate critical path and linear scheduling computation to accurately update the project's time and cost during construction.

Using a case study of a road project, the paper proves that the proposed framework reduces as-built documentation effort and its schedule updates are more responsive to all as-built events than traditional scheduling techniques.

The proposed method applies to linear projects (e.g. highways) and can be extended to other repetitive projects such as high-rise buildings. It can also be extended to include voice features and procedures for forensic schedule analysis.

The developed methodology presents a low-cost approach to document timely progress information for decision makers of massive linear projects (often associated with infrastructure) to have better control over the execution of projects, save documentation time and cost, and avoid disputes and problems.

This research contributes in improving construction productivity by collecting timely as-built information using affordable communication technologies. It also presents novel advancements to the existing scheduling and control techniques to suit linear projects, which are most challenging.

The purpose of this paper is to study the application of feature‐based image matching algorithm for PCB matching without using special fiducial marks.

Speed‐up robust feature (SURF) is applied to extract the interest points in PCB images. An advanced threshold is set to reject the interest points with low responses to speed up feature computation. In order to improve the performance for rotation, the descriptors are based on multi‐orientations. The many‐to‐many tentative correspondences are determined with a maximum distance. Hough transform is used to reject the mismatches and the affine parameters are computed with a square‐least solution.

Results show that the method proposed in this paper can match the PCB images without using special fiducial marks effectively. The image matching algorithm shows a better performance for image rotation than the standard SURF and it succeeds in matching the image including repetitive patterns which will deteriorate the distinctiveness of feature descriptors.

Additional orientations produce more descriptors so that it takes extra time for feature description and matching.

The paper proposes a SURF‐based image matching algorithm to match the PCB images without special fiducial marks. This can reduce the complexity of PCB production. The image matching algorithm is robust to image rotation and repetitive patterns and can be used in other applications of image matching.

The benefits of integrated design and manufacturing systems in mid to high volume production are widely accepted. The low production volume, large component size and high complexity product domain is less well served by this technology. Examples of this kind of product are machine tools, ships, aircraft and buildings. Design for assembly techniques are a method by which design and manufacture can be integrated and aid the realization of full‐process concurrent engineering. Quantifies the significance of this product domain to the UK economy, describes the particular nature of these designs for assembly processes, reviews existing work in the field and specifies the problems encountered. Defines an industrial and academic solution programme. Proposes an integrated approach to applying design for assembly to this repetitive direct engineering domain.

The purpose of this paper is to address the need for increased understanding, awareness and recognition of the autism female phenotype in terms of repetitive behaviours and restricted interests (RBRIs).

A systematic PRISMA review was conducted. The main aim of the present systematic review is to identify studies which have investigated RBRIs in females with autism spectrum disorder (ASD) or the differences in RBRIs between males and females with ASD.

In sum, 19 relevant articles were identified: 5 studies found no significant evidence to support the notion of sex differences in RRBIs in ASD; 1 study did not report any differences in RRBIs between males and females with ASD; 12 studies found evidence that males with ASD had significantly more RRBIs compared to females with ASD; and, lastly, 1 study found that girls with ASD have features of RRBIs which are exhibited more compared to boys with ASD.

There is a real lack of in-depth knowledge and understanding of the female phenotype of ASD, and such lack of knowledge has a detrimental impact on the identification of autistic females and a lack of identification can have negative consequence. This is important to address in future research as it is well established that the earlier the diagnosis, the better the outcomes, due to the timely access to appropriate interventions.

The RBRIs exhibited in autistic females are not sufficiently captured by most currently diagnostic instruments. Clinicians are less likely to identify the RBRIs in females as they tend not to be the typical repetitive behaviours commonly associated with ASD. It has been recommended that clinicians consider “females as a whole” in terms of their clinical presentation and look for any indication of RBRIs, even repetitive interests which appear clinically innocuous.

There is relatively little research investigating RBRIs in autistic women and girls. There is a real need to highlight the importance of understanding and recognising how RBRIs can differ between males and females with ASD.