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Artificial neural networks have been effectively used in various civil engineering fields, including construction management and labour productivity. In this study, the performance of the feed forward neural network (FFNN) was compared with radial basis neural network (RBNN) in modelling the productivity of masonry crews. A variety of input factors were incorporated and analysed. Mean absolute percentage error (MAPE) and correlation coefficient (R) were used to evaluate model performance. Research results indicated that the neural computing techniques could be successfully employed in modelling crew productivity. It was also found that successful models could be developed with different combinations of input factors, and several of the models which excluded one or more input factors turned out to be better than the baseline models. Based on the MAPE values obtained for the models, the RBNN technique was found to be better than the FFNN technique, although both slightly overestimated the masons' productivity.

One of the concrete examples of industrial symbiosis development is eco-industrial parks, which improves resource efficiency and minimizes environmental impacts by adopting models for waste exchanges between industries. Despite past efforts, many industrial zones around the world are not yet considered as eco-industrial parks because of the low number (or total lack) of symbiotic relationships among industries. A promising strategy is to develop those existing industrial zones into eco-industrial parks. However, there is a lack of studies addressing how to assess the environmental improvement in relation to network sustainability. This study demonstrates such an assessment approach using an integration of food web analysis and social network analysis. These two methods can assist in assessing differences in network configurations with respect to potential implementations of industrial symbiosis, and in analysing the resilience, redundancy, connectance, and cyclicity of eco-parks. The use of the methods is illustrated in a case study of an industrial zone in Turkey. Four potential future scenarios are proposed including potential future co-location of companies in the industrial zone in order to foster industrial symbiotic network formation. These scenarios are compared with the current configuration. The results indicate the method's ability to assess the resilience of an industrial network. Moreover, the case shows an improvement of network sustainability and follows some sustainable properties of natural ecosystems as a result of implementing the industrial symbiosis.

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The current public procurement law (Law No. 4734) was established in Turkey in 2003. The current law has fundamental differences from the previous one, Law No. 2886, in that the current law's main objective is to increase the effective use of public resources. Although the current law was enacted nine years ago, no in-depth research has been undertaken related to the extent of public savings. Thus, the aim of this research is to analyze the performance of public investments for construction with respect to their success in achieving on time and within budget completion. Additionally, a comparison between the completion duration and budget of construction projects undertaken under the current and the previous law is presented. To achieve these goals, historical contract documents addressing 878 and 575 public construction projects undertaken under two laws, respectively, were analyzed. In this context, the data mining method, including decision trees, artificial neural network, and support vector machines, was applied to predict the duration and cost deviations of the construction projects during the tender process, and the results were compared. It was demonstrated that the current law has contributed substantially towards the completion of the projects within estimated or envisaged durations and costs. The findings of this research can be generalized to countries with similar economical and organizational structures with Turkey.

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