The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Vermiculite bio-barriers for Cu and Zn remediation: an eco-friendly approach for freshwater and sediments protection

The increase in heavy metal contamination in freshwater systems causes serious environmental problems in most industrialized countries, and the effort to find ecofriendly techniques for reducing water and sediment contamination is fundamental for environmental protection. Permeable barriers made of natural clays can be used as low-cost and eco-friendly materials for adsorbing heavy metals from water solution and thus reducing the sediment contamination. This study discusses the application of permeable barriers made of vermiculite clay for heavy metals remediation at the interface between water and sediments and investigates the possibility to increase their efficiency by loading the vermiculite surface with a microbial biofilm of Pseudomonas putida, which is well known to be a heavy metal accumulator. Some batch assays were performed to verify the uptake capacity of two systems and their adsorption kinetics, and the results indicated that the vermiculite bio-barrier system had a higher removal capacity than the vermiculite barrier (?34.4 and 22.8 % for Cu and Zn, respectively). Moreover, the presence of P. putida biofilm strongly contributed to fasten the kinetics of metals adsorption onto vermiculite sheets. In open-system conditions, the presence of a vermiculite barrier at the interface between water and sediment could reduce the sediment contamination up to 20 and 23 % for Cu and Zn, respectively, highlighting the efficiency of these eco-friendly materials for environmental applications. Nevertheless, the contribution of microbial biofilm in open-system setup should be optimized, and some important considerations about biofilm attachment in a continuous-flow system have been discussed.