Assessment of Construction Risk Management Maturity Using Hybrid Fuzzy Analytical Hierarchy Process and Fuzzy Synthetic Approach: Iraq as Case Study

Knowing and developing the construction organizations' maturity level in risk management is critical to ensure they achieve their strategic objectives. This paper aims to design a new construction organizations' risk-management maturity model (C.ORM3) using new hybrid techniques and a distinct validation strategy based on global and local experience, to assess risk management maturity level in developing countries. A multi-steps methodology was adopted in this research. The study adopted an excessive systematic literature reviews of 22 previous articles on RM maturity and four standards and guidelines for eliciting model components. These components include five attributes with 26 capabilities;24 capabilities identified from literature review and 2 from experts. These capabilities are evaluated against five levels: immature, ad-hoc, standard, managed, and optimized. The authors adopted a new strategy for validating the model by three groups of global and local experts and verifying the proposed model in a realistic-world case study. This study is the first to use a hybrid method based on the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Synthetic Evaluation (FSE) techniques in evaluating RM maturity (RMM). Iraqi construction organizations validate the practicality of the model. The results showed that the overall RMM level of the Iraqi construction sector is 1.52, between immature and ad-hoc. The model has been converted into a computer template for ease of use by organizations. This study concluded that the suggested C.ORM3 helpful for construction organisations to evaluate their current state of RM and plan for future development © 2023, Mathematical Modelling of Engineering Problems.All Rights Reserved.

CoughNet-V2: A Scalable Multimodal DNN Framework for Point-of-Care Edge Devices to Detect Symptomatic COVID-19 Cough

With the emergence of COVID-19 pandemic, new attention has been given to different acoustic bio-markers of the respiratory disorders. Deep Neural Network (DNN) has become very popular with the audio classification task due to its impressive performance for speech detection, audio event classification etc. This paper presents CoughNet-V2 - a scalable multimodal DNN framework to detect symptomatic COVID-19 cough. The framework was designed to be implemented on point-of-care edge devices to help the doctors at pre-screening stage for COVID-19 detection. A crowd-sourced multimodal data resource which contains subjects' cough audio along with other relevant medical information was used to design the CoughNet-V2 framework. CoughNet-V2 shows multimodal integration of cough audio along with medical records improves the classification performance than that of any unimodal frameworks. Proposed CoughNet-V2 achieved an area-under-curve (AUC) of 88.9% for the binary classification task of symptomatic COVID-19 cough detection. Finally, measurement of the deployment attributes of the CoughNet-V2 model onto processing components of an NVIDIA TX2 development board is presented as a proposition to bring the healthcare system to consumers' fingertips. Clinical relevance - CoughNet-V2 will help medical practitioners to asses whether the patients need intensive medical help without physically interacting with them. © 2022 IEEE.

Analyzing the Impact of the COVID-19 Pandemic Risks on Construction Projects in Developing Countries: Case of Iraq

The construction industry has been exposed to many risks emerging from the COVID-19 pandemic. This can be seen from project schedule delays, cost overrun, legal claims to supply chain distribution, and workforce restrictions. To this end, the purpose of this study is to identify and analyze the risks emerging from the COVID-19 pandemic for the construction industry in developing countries by considering the case of the Iraqi construction industry. To achieve this, a mixed-methods approach was adopted as follows: (1) focus group session with eight construction experts to identify the risks emerging from the COVID-19 pandemic;(2) development of Analytic Hierarchy Process (AHP) method based on probability and impact risk matrix by considering two additional dimensions, namely Estimated Recovery Time Level (ERTL), and Expected Cost Level (ECL);and (3) survey data collection from 41 Iraqi construction experts to quantify the identified risks. Ultimately, a total of 29 risks were identified and categorized into four construction themes, namely contractual/legal implications, construction financial market, supply chain operations, and health and safety of the construction workforce. The findings show that the most significant COVID-19 pandemic risks are claims arising under a construction contract, virus spreading due to poor hygiene and lack of sanitizing stations, and contractor bankruptcy. This research offers a better understanding of the critical risks emerging from COVID-19, which can be used as a reference for researchers and industry stakeholders in planning to address the impact of the COVID-19 pandemic risks during the life cycle of a construction project.