The construction industry is traditionally environmentally unfriendly. The environmental impacts of construction waste include soil contamination, water contamination, and deterioration of landscape. Also, construction waste has a negative economic impact by contributing additional cost to construction due to the need to replace wasted materials. However, in order to mitigate waste, construction managers have to explore management options, which include reduction, recycling, and disposal of wastes. Reduction has the highest priority among the waste management options but efficient reduction cannot be achieved without adequate identification of the sources of waste. Therefore, the purpose of this paper is to present a study that was carried out on the contribution rates of nine identified sources of construction waste. Establishing the contribution rates of different waste sources will enhance knowledge-based decision-making in developing appropriate strategy for mitigating construction waste. Quantitative research method, using survey questionnaire, was adopted in this study to assess the frequency and severity of contribution of the sources of waste. As one of the findings of the study, residual waste such as material off-cuts was identified as the highest contributor to construction waste. This study consequently demonstrated that waste has a significant contribution to the cost of construction.
Due to the rapid growth of population in the last 20 years, an increased number of instances of heavy recurrent traffic congestion has been observed in cities around the world. This rise in traffic has led to greater numbers of traffic incidents and subsequent growth of non-recurrent congestion. Existing incident detection techniques are limited to the use of sensors in the transportation network. In this paper, we analyze the potential of Twitter for supporting real-time incident detection in the United Kingdom (UK). We present a methodology for retrieving, processing, and classifying public tweets by combining Natural Language Processing (NLP) techniques with a Support Vector Machine algorithm (SVM) for text classification. Our approach can detect traffic related tweets with an accuracy of 88.27%.
Purpose – The purpose of this paper is to discuss an integrated decision analysis framework for the investment justification of implementing alternative information and communication technology (ICT)-based logistics systems in the construction industry so as to enhance the decision-making process in selecting the best alternative. Design/methodology/approach – An integrated framework is proposed that is composed of a set of interrelated evaluation and analysis techniques that allow the identification and quantification of costs, benefits and risks involved in implementing ICT systems to mitigate problems that hinder the efficient operation of construction logistics. Such techniques include decision trees and multi-attribute decision-making under uncertainty that can be applied to the logistics planning of any new build construction project. Findings – The probabilities of providing benefits vary among the alternatives, and the probabilities will replace the uncertainties surrounding the impacts of the alternative ICT systems in addressing the identified construction logistics problems with chance events so as to estimate the expected cost of each alternative with respect to each selection attribute. Practical implications – This paper shows that it is almost certain that the analysed alternative ICT system will provide benefit because its probability of benefit is almost equal to 1. Originality/value – The framework captures the existing problems of logistics in construction process, potential solution that can address the problems through the implementation of ICT systems and the decision-making process in the selection of appropriate ICT solution. The output of the framework will help to make knowledge-based decision in selecting the best ICT system for addressing construction logistics problems.
Purpose -The purpose of this paper is to discuss assessment of the satisfaction levels of different members of a construction project team as a basis for meeting the needs of the client. Design/methodology/approach -An integrated framework is proposed that enables a collaboration of construction clients and project participants based on the recognition of the satisfaction requirements of every participant represented in the project team. The framework is developed to prioritise the satisfaction attributes of flexible number of construction clients and project participants, and enables the integration of these participants and their satisfaction attributes using mathematical and engineering techniques. Findings -The framework can be applied at the different phases of the project life cycle. In addition, the satisfaction levels of construction clients and the project participants can be enhanced by focussing on the values of their satisfaction attributes and improving the integration of the project team. Practical implications -The paper shows that a collaboration of construction clients and project participants based on the recognition and acknowledgement of each participant and their requirements is essential to improving project satisfaction in the construction sector. Originality/value -The framework captures and analyzes the level of integrated project team satisfaction. The outcome of the study will improve understanding the satisfaction requirements of every client and participant represented in a given construction project team.
Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.
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