Predicting the duration of construction projects with acceptable accuracy is a problem for contractors and researchers. Numerous researchers and tools are involved in sorting out this problem. The aim of the study is to predict the construction duration using four analytical tools as an approach. The success of construction projects in regard to time depends on various factors such as selection of contractors, consultants, cost of the projects, quality of the projects, the quantity of the projects, environmental factors, etc. Presently available commercial tools in the market are not designed as universally common and concerned. Every tool performs well in a particular situation. The prediction of India’s highway road projects duration is the biggest construction issue in the country due to various reasons. To overcome this problem, the methodology of the paper adopts various strategies to find suitable tools to predict the highway road projects’ duration, in which it classifies and analyzes the collected data. As a part of this work, the details of 363 government infrastructure projects (traditional procurement) were collected from 2000 to 2018. The present study also adopts various tools for duration prediction such as artificial neural networks (ANNs), smoothing techniques, time series analysis, and Bromilow’s time–cost (BTC) model. The results of the study recommend smoothing techniques with a constant value of 0.3, which gave the remarkable very small error of 1.2%, and its outcomes become even better when compared to other techniques.

The Construction Industry Development Council (CIDC) of India has been calculating and publishing the Construction Cost Index (CCI), monthly, since 1998. Construction cost variations interrogate different kinds of projects such as roads, power plants, buildings, industrial structures, railways and bridges. The success rate of completion of construction project is diminished due to the lack of prediction knowledge in CCI. Predicting CCI in greater accuracy is quite difficult for contractor and academicians. The following factors are influenced higher in CCI such as population, unemployment rate, consumer price index (CPI), long term interest rate, domestic credit growth, Gross Domestic Product (GDP) and money supply (M4). CCI can be used to forecast the construction cost. The relevant resource data was collected across the nation between 2003 and 2018. As outcome-based, non-econometric tools such as smoothing techniques, artificial neural network (ANN) and support vector machines (SVMs) have produced a better outcome. Among these, smoothing techniques have given the notable low error and high accuracy. This accuracy has measured by Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The major objective of this research is to help the cost estimator to avoid underestimation and overestimation.

It is well known that the civil engineering constructions are subjected to cost risk and time overruns. The uncertainties of the cost of construction many times result in disputes among stakeholders. The recent cost fluctuation in sand price in Tamil Nadu is a good example of time and cost overruns. There are too many models developed to predict the cost of construction by using different parameters and tools. The objectives of this research are to analyse the importance of research in this field, the countries focusing on this issue, level of implementation by the practicing engineers, the tools often or successfully used, the difficulties in predicting the cost and the accuracy of prediction and bringing out a useful conclusion to provide the direction for future research. In this research, a sample of 324 research papers out of more than 2000 papers listed in Scopus database between the years 1990 and 2015 were considered and analyzed on five factors. The five factors are 1) authors affiliation – academics, industry or both; 2) country; 3) tools used – ANN, regression, time-series models, etc.; 4) complexity involved or ease of use; 5) accuracy of results. The results show interesting information.

Plastic is a commonly used and perhaps unavoidablematerial due to its multifaceted nature. Plastic wastes do not degrade easily and hence present as a major threat to environment. Plastics of particle size less than 5mm is universally considered as microplastics. The present study investigates the identification and identification and quantification of microplastics. The sample was collected from the wastewater treatment plant of the Kalasalingam University campus as a bulk sample. The sample was prepared using Hydrogen peroxide and Iron II sulfate to oxidize the organic matter. Filtration was carried out in a set of filetr papers arranged in series with decreasing pore size. Sediments were collected and analyzed using FTIR imaging, The surface of the paper was analyzed using 40X dissecting microscope for visual identification. Further, SEM analysis with EDS mapping was performed to study the material composition. Eight different types of microplastics (MPs) were identified and sizes measured. The particle size varied from 10 -20 micron.

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