Construction schedule risk analysis – a hybrid machine learning approach

The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.

A hybrid approach for the efficient computation of polycrystalline yield loci with the accuracy of the crystal plasticity finite element method

Direct experimental evaluation of the anisotropic yield locus of a given material, representing the zeros of the material's yield function in the stress space, is arduous. It is much more practical to determine the yield locus by combining limited measurements of yield strengths with predictions from numerical models based on microstructural features such as the orientation distribution function (ODF; also referred to as the crystallographic texture). For the latter, several different strategies exist in the current literature. In this work, we develop and present a new hybrid method that combines the numerical efficiency and simplicity of the classical crystallographic yield locus (CYL) method with the accuracy of the computationally expensive crystal plasticity finite element method (CPFEM). The development of our hybrid approach is presented in two steps. In the first step, we demonstrate for diverse crystallographic textures that the proposed hybrid method is in good agreement with the shape of the predicted yield locus estimated by either CPFEM or experiments, even for pronounced plastic anisotropy. It is shown that the calibration of only two parameters of the CYL method with only two yield stresses for different load cases obtained from either CPFEM simulations or experiments produces a reliable computation of the polycrystal yield loci for diverse crystallographic textures. The accuracy of the hybrid approach is evaluated using the results from the previously established CPFEM method for the computation of the entire yield locus and also experiments. In the second step, the point cloud data of stress tensors on the yield loci predicted by the calibrated CYL method are interpolated within the deviatoric stress space by cubic splines such that a smooth yield function can be constructed. Since the produced yield locus from the hybrid approach is presented as a smooth function, this formulation can potentially be used as an anisotropic yield function for the standard continuum plasticity methods commonly used in finite element analysis.

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Development Strategy of Palu Bay Marine of Sustainable Tourism with the A'WOT Hybrid Method

Sustainable development has become one of the strategic issues in the tourism industry. Sustainability is an action that combines environmental, sociocultural, and economic concepts. The tourism industry has had good benefits on economic and social growth. Still, if it is not adequately planned and managed, it can have harmful consequences on the environment. This research aims to present a sustainable development strategy for Palu Bay Marine Tourism following a natural disaster. The A'WOT hybrid method (AHP-SWOT) was used to achieve the research objectives. The A'WOT method is a combination of a SWOT analysis and the AHP method. This research was done on Palu Bay tourism, Central Sulawesi Province, Indonesia. This research resulted in a sustainable development strategy for Palu Bay tourism related to the diversity of products and improvement of event management, an improved image of Palu Bay tourism, improvement in the visitor management system to minimize environmental impacts, and efficient and effective promotion and branding. Received: 14 October 2021 / Accepted: 29 November 2021 / Published: 3 January 2022

Conducting a FIMA Lifesaver Course in the COVID-19 Setting by IMAM in Malaysia

Every year, the Federation of Islamic Medical Association (FIMA) conducts a basic life support (BLS) course for the public, not just in one, but in several countries. It is held in mosques as a method of raising awareness on the importance of BLS among the public, apart from highlighting the function of a mosque as a place of obtaining knowledge. Traditionally, it was conducted as face-to-face training. However, with the 2019 novel coronavirus pandemic, the training was changed to a hybrid method to balance between the needs to teach BLS skills to the public and the necessity of avoiding the spread of infection. This article discussed the Islamic Medical Association of Malaysia (IMAM)’s experience in organizing a mass BLS course for public in the midst of the COVID-19 pandemic while utilising a small mosque as a hub of learning.International Journal of Human and Health Sciences Vol. 06 No. 01 January’22 Page: 6-10