Modelling supplier selection and material purchasing for the construction supply chain in a fuzzy scenario-based environment

Mathematical relations between supplier capacities, the resulting material supply shortages, together with the impact of material delays on construction projects are not well defined. In response to this, this paper presents a novel multi-objective mixed integer linear programming model that considers the selection of suitable suppliers, inventory management practices, order quantities and the possibility of splitting a material order as integrated decisions to be optimised. The trade-off between the overall procurement cost and the weighted lateness, a measure of material delay impacts, is optimised. Material prices, supplier capacities, and resulting delays are treated as fuzzy scenario-based parameters. The proposed model is tested on a numerical example and computation experiments validate the model performance. An extensive sensitivity analysis is carried out and results suggest that by considering high variations in uncertain supplier capacities, the model would generate lower procurement cost and show less significant delay impacts. Whereas greater variations in uncertain material prices cause the total procurement cost to grow 55%; greater variations in uncertain delay durations also drastically increase the weighted lateness by over 70%. This highlights the importance of having high quality estimates for uncertain parameters. Additionally, the analysis also indicates that a minimum overall satisfaction level of 0.9338 can be achieved depending on the model user's strategies, and the proposed scenario-adjusted problem outperforms problems modelled under deterministic market conditions. The major contribution of this paper lies in the development of a fuzzy scenario-based model to solve the supplier selection and material purchasing problem in construction supply chains

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

The job-seeking experiences of migrants and refugees in the Australian construction industry

International migrant and refugee numbers are at record levels and continue to grow. The construction industry is a major source of potential employment for migrants and refugees and emerging social and sustainable procurement policies in many parts of the world are also requiring construction supply chains to employ refugees and migrants as a condition of public sector contracts. However, there is virtually no research into the barriers that refugees and migrants face in seeking decent employment in the construction sector. Addressing this important gap in knowledge an exploratory survey of refugees' and migrants' job-seeking experiences in the Australian construction industry is presented. Results show that by far the greatest barrier to employment is lack of construction industry experience, followed by poor recognition of previous skills and experience. Recent migrants and refugees and those with the greatest previous experience of working in construction face the greatest barriers in finding decent work. Men from an Arabic background also experience greater difficulties than other cultural groups. It is recommended that policy-makers develop more initiatives to provide work experience and engage with construction employers about the challenges which refugees and migrants face in finding work in construction

Barriers to employment for refugees seeking work in the Australian construction industry: an exploratory study

Purpose: In Australia, as in many other countries, refugees are over-represented in the ranks of the unemployed, under-employed and precariously employed and often become frustrated in their attempts to secure work. Despite the construction industry being a major potential source of employment for refugees, there has been a surprising lack of research into their experiences of securing work in the industry. Addressing this gap and also the general lack of voice for refugees in construction research, the aim of this paper is to explore the barriers refugees face in securing employment in the construction industry. Design/methodology/approach: This paper reports a survey of refugees who have worked or attempted to seek work in the Australian construction industry. Findings: Results show that the main barriers to securing employment in construction are: lack of local work experience; employer discrimination; employer failure to recognise previous qualifications, skills and experience and employers not understanding the challenges they face. Government employment agencies and systems are also perceived to be of limited value and overly complex, in contrast to the activities of not-for-profit support agencies. Research limitations/implications: While the research is limited to Australia, the findings contribute an important and missing refugee dimension to the emerging body of research on construction social procurement. They also contribute unique sector-specific insights into the broader debate about refugee resettlement and employment. Further research is needed in other national contexts. Practical implications: Recommendations are made to address the barriers to employment identified including: initiatives to provide refugees with work experience in the industry; education to break-down negative stereotypes of refugees among employers; greater support for not-for-profits supporting refugees and reform of government and employment agency systems and procedures. Social implications: By enhancing understanding of the barriers to employment for refugees in construction and proposing solutions to reduce those barriers, this research contributes new insights into a growing global challenge of how we better integrate growing numbers of refugees into harmonious and prosperous societies. Originality/value: The findings are important in facilitating the smoother integration of refugees into society. Beyond the moral imperative, there are significant social, cultural and economic benefits which successful refugee integration brings to host countries and industries like construction which in many countries are now being required to employ refugees in their workforce as a condition of public sector contracts

A neural network approach to predicting the net costs associated with BIM adoption

A neural network approach is proposed to estimate the costs and benefits associated with implementing Building Information Modelling (BIM) at firms. This includes specifying the BIM applications and the resources required for reaching a specific level of detail within the generated models, referred to as level of development (LOD). Such predictions are imperative to decision makers and can aid in the examination of the best strategies to execute when deciding on the adoption and implementation of BIM. The proposed neural network is customised to suit a firm's investment plan when it comes to BIM implementation. Multi-label and multi-class classifications are adopted to derive the cost and benefit functions for BIM application and LOD implementation, respectively. Threshold functions to distinguish the positive and negative labels in multi-label classification are adopted. The proposed neural network is developed based on data collected from Australian and Chinese construction firms using a 7-point Likert type questionnaire. The proposed neural network provides decision-makers with a tool to assess which BIM/Non-BIM applications to implement, along with the LOD that is most suited to the organisation's financial and technical ability

2nd International Conference on Sustainable Development in Civil Engineering (ICSDC 2019), Jamshoro Pakistan

Floods are natural disasters and pose a threat to the lives, property, and infrastructure of an urban area. Though their risk cannot be fully eliminated, several methods can be used to manage floods, once they occur. This includes identification of flood-prone areas, timely detection of the affected areas, mapping rescue routes and arranging logistics to carry out the rescue as soon as possible. The use of advanced innovative technologies for flood management such as image detection and machine learning can assist in effective flood management. This paper presents a novel approach through the integration of image processing and machine learning to detect flood-affected areas using a set of images. The three-step approach proposed in this study is based on landmark detection from images, training of a machine learning algorithm and classifying images from an area as flooded or non-flooded. The results based show an accuracy level of 90% depicting the significance of the proposed model for image-based flood detection

Optimising embodied energy and thermal performance of thermal insulation in building envelopes via an automated building information modelling (BIM) tool

Insulation systems for the floor, roof, and external walls play a prominent role in providing a thermal barrier for the building envelope. Design decisions made for the insulation material type and thickness can alleviate potential impacts on the embodied energy and improve the building thermal performance. This design problem is often addressed using a building information modelling (BIM)-integrated optimisation approach. However, one major weakness that lies in the current studies is that BIM is merely used as the source for design parameters input. This study proposes a BIM-based envelope insulation optimisation design tool using a common software Revit and its extension Dynamo to find the trade-off between the total embodied energy of the insulation system and the thermal performance of the envelope by considering the material type and thickness. In addition, the tool also permits data visualisation in a BIM environment, and automates subsequent material library mapping and instantiates the optimal insulation designs. The framework is tested on a case study based in Sydney, Australia. By analysing sample designs from the Pareto front, it is found that slight improvement in the thermal performance (1.3399 to 1.2112 GJ/m2) would cause the embodied energy to increase by more than 50 times.</jats:p

An integrated approach for post-disaster flood management via the use of cutting-edge technologies and UAVs: A review

Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach