Evaluation of BIM Education for Quantity Surveying: A Review of Teaching Approaches

Building Information Modelling (BIM) technology has become increasingly well-known among construction industry players throughout the world. It is known as a process that offers numerous benefits by the implementation in the industry. Almost 50% of practitioners in construction industry is using BIM. Quantity Surveyors (QS) are one of the important main professionals in construction industry who should have an adequate and sufficient BIM knowledge and skills. Equivalent to this, based on the educational perspective it is clear that there is a growing need for universities to provide their graduates with appropriate BIM-related skills. Although the educational frameworks that have been established for academic purposes in Malaysia, still, it is in doubt whether this framework is parallel with industry’s demand and also whether the knowledge and skills provided sufficient with industry’s requirements. It is crucial to recognize the applicable teaching approach for BIM educations in order to ensure students capable in applying BIM tools and meet the expectation of industry. This paper reviews the BIM teaching approaches and the BIM module applied in QS undergraduate program.   Keywords: BIM, quantity surveyors, QS education, teaching approach, Malaysi

Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates.

Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis

The major causes of research misconduct

Researchers’ integrity is of the important roles as a responsible researcher as their main job scope is finding, presenting, and reporting new solutions for every aspect of life to the world. This integrity will receive serious damage because of misconduct acts among unethical researchers because of personal gains or fame. Research misconduct (RM) has been defined as fabrication, falsification, and plagiarism of scientific data along the process of completing the research objective. This study aims to discuss the type and cause of research misconduct and ways to overcome it. The causes of RM due to poor supervision, personal circumstances, inadequate training and competitive pressure. In order to overcome RM two parties are responsible which involve the researcher and administrative department. In addition, advanced technology applications i.e. Turnitin (Internet-based plagiarism detection) could help the administration to monitor the RM activities. Hence, the early awareness of RM would create quality research and responsible researchers in future and among the public