The concept of value management (VM) is becoming more relevant to Sri Lankan construction industry. Value Management provides structured, documentable consideration of project stakeholders to ensure that projects are required, framed to satisfy values and sufficiently supported by all stakeholders to ensure successful completion (Austin and Thomson 2001). This is supported by the definition of Albert and Betty (1996) that VM is a structured, systematic, flexible, team oriented approach for assessing the relationship between function, cost and worth. The aim of this paper is to illustrate the development and increasing relevance of VM in the Sri Lankan construction industry in the last decade and to describe the VM systems which have developed within the industry. An outline also is given of the objectives of VM and the methods developed for the application of VM. The paper concludes with the identification of benefits of the process and justifying its relevance with brief case studies which had been carried out to demonstrate the success of the process.

Highways play a significant role in the economic growth of developing countries. Sri Lankan government also has realized such importance and has directed special focus on constructing new highways. However, highways construction is not a simple task and these projects are often typified by risk and complexities which create a range of problems that has to be dealt with utmost care. Variations are one of them, which commonly occur due to uncertain scopes of work defined at the beginning. Thus, this study is undertaken to identify causes, nature and effects of variations in highways construction in Sri Lanka. A questionnaire survey was undertaken to identify frequent causes of variations and semi-structured interviews were conducted to capture data regarding nature and effects of variations. The results revealed that change in mind force and requirement increases were the main causes of client originated variations while design changes and defects in BOQ were the main causes of consultant originated variations. Land acquisition and funds arranging issues were identified as main unforeseeable cause that originates variations. The study further revealed that omission of any work has a significant impact on the nature of variations. In most situations, variations have resulted in cost overruns with an average increase of 9.9% of the initial contract sum.

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COVID-19 was announced as a global pandemic by the World Health Organization (WHO) in March 2020. With more than 31.3 million confirmed cases and over 965 thousand deaths recorded as of September 2020, it has inflicted catastrophic damage worldwide. The aim of this study is to develop an algorithm based on artificial intelligence (AI) and image processing techniques to identify COVID-19 patients with the aid of CT chest scan images. This study used a CT scan image dataset that is publically available for the researchers at Kaggle. We randomly extracted 27% of positive CT (pCT) images and 11% of negative CT (nCT) images from the original dataset. In the testing process, 120 of the test subjects in both nCT and pCT were used to validate the algorithm. Based on the experimental findings, the proposed COVID-19 detection algorithm shows promising results for the identification of COVID-19 patients with 90.83% accuracy at an average precision of 0.905.