Systematic Representation of Relationship Quality in Conflict and Dispute: for Construction Projects

The construction industry needs to move towards more relational procurement procedures to reduce extensive losses of value and avoid conflicts and disputes. Despite this, the actual conceptualization and assessment of relationships during conflict and dispute incidents seem to be neglected. Via a review of literature, relationship quality is suggested as a systematic framework for construction projects. General system theory is applied and a framework consistent of four layers respectively labelled as triggering, antecedent, moderation and outcome is suggested. Two different case studies are undertaken to represent the systematic framework; which verifies that changes in contracting circumstances and built environment culture can affect the identified layers.Through system reliability theories a fault tree is derived to represent a systematic framework of relationship quality. The combinations of components, causes, and events for two case studies are mapped out through fault tree. By analysing the fault tree the combination of events that lead to relationship deterioration may be identified. Consequently the progression of simple events into failure is formulized and probabilities allocated. Accordingly the importance and the contribution of these events to failure become accessible. The ability to have such indications about relationship quality may help increase performance as well as sustainable procurement. Paper Type: Research articl

Modeling, Designing and Applying Machine Learning Algorithms for Driver Drowsiness Detection

Driver drowsiness has been a significant hazard resulting in various traffic accidents. Therefore, monitoring this condition is crucial not only in alerting drivers, but also in avoiding fatal accidents. Many research studies propose new systems to reduce the number of drowsiness-related injuries and fatalities. The ultimate goal for a drowsiness detection system is to detect the drowsiness on time and minimize the system or environment errors to avoid false readings, such as studying physiological signal processing patterns. These potentially life-saving systems must operate in a timely manner with the highest precision. Researchers proposed various methods based on driving pattern changes, driver body position, and physiological signal processing patterns. There is a focus on human physiological signals, specifically the electrical signals from the heart and brain. In this study, we are presenting an alternative method to determine and quantify the driver drowsiness levels using a physiological signal that was collected in a non-intrusive method. This methodology utilizes heart rate variation (HRV), electrocardiogram (ECG), and machine learning for drowsiness detection. It is apparent that a driver’s drowsiness is associated with an immediate change in heart rate, and due to the fact that Electrocardiogram (ECG) is used to detect an accurate heart rate. We used it as a parameter in the proposed design where it consists of a non-contact ECG sensor as an input source and a circuit with a two-stage amplifier to improve the ECG signal’s strength and filters to minimize noise. An approximate maximum peak ECG output voltage of 2.8V was obtained in LT Spice, and the resulting ECG output is sufficient enough to detect a driver’s drowsiness while preventing major accidents. Furthermore, the HRV is measured with an ECG. The algorithm uses both wavelet and short Fourier transform (STFT). The algorithm extracts and selects the desired features. Then, the system applies both the support vector machine (SVM) and K- nearest neighbor (KNN) method. This achieves an accuracy of 80% or higher. In this research, the accuracy output for the SVM method is 83.8%, 82.5% when using STFT, and 87.5% when applying the WT technique. The algorithm with highest accuracy helps to decrease the number of accidents due to drowsiness. Furthermore, we applied unsupervised machine learning (clustering) to study the behavior of HRV during drowsiness. We can measure different levels of drowsiness based on the changes in the density and shape of the HRV clusters by using this method. Moreover, the pre-measured labeled data is not required to establish the algorithm in this method. Therefore, this algorithm evaluates drowsiness and no prerecorded data is required for any unknown object or person. Successful application of this drowsiness detection method may help to avoid traffic accidents. This study may be beneficial for policy maker’s in preparing regulations to prevent traffic accidents worldwide and may also helpful for users to increase their knowledge and awareness regarding drowsiness detection. Keywords: Drowsiness, Machine Learning, Electrocardiogram (ECG), Heart Rate Variability (HRV), Wavelet Transform (WT)

Application of Constructability Concepts in the Industrialised Building System for the Malaysian Construction Industry

Constructability is generally reducing the problems of construction by incorporating the construction knowledge into the design of a construction project. The Malaysian construction industry is attempting to promote and use Industrialized Building systems (IBS) for better construction practice with more effectiveness and efficiency, but in terms of constructability and research into the application of constructability concepts for IBS little work has been done. In fact the Malaysian construction industry is still not applying the concepts of constructability in totality and there is lack of constructability research in Malaysia. In this research the application status of constructability concepts which have been previously defined for Malaysia are examined and assessed within the Malaysian IBS industry. The ease of constructability application of IBS and conventional building methods are investigated and finally the concepts that are not being applied up to their potential level in IBS construction and resemble possible problems in the process of application are identified. A survey is used to obtain the essential data needed for the research from the active IBS industry participants of Malaysia. It was found that the IBS contractors are applying constructability more than the designers and suppliers and also the early constructability concepts gained a higher application score than other concepts. Using information technology and the innovative concepts of the field operation phase were the most difficult concepts to apply and generally the application of constructability concepts in IBS construction is easier compared to the conventional building systems according to the IBS industry participants