Mechanical and thermal properties of a biocomposite based on polyvinylchloride/epoxidized natural rubber blend reinforced with rice husk microfiller

International audienc

A Numerical Modeling of RC Beam-Column Joints Compared to Experimental Results

International audienc

Nitrogen removal in subsurface constructed wetland: Assessment of the influence and prediction by data mining and machine learning

Subsurface constructed wetland (SCW) appears to be an economical and environmental-friendly practice to treat nitrogen-enriched (waste) water. Nevertheless, the removal mechanisms in SCW are complicated and rather time-consuming to conduct and assessment the efficiency of new experiments. This work mined data from literature and developed the machine learning models to elucidate the effect of influent inputs and predict ammonium removal rate (ARR) in SCW treatment. 755 sets and 11 attributes were applied in four modeled algorithms, including Random forest, Cubist, Support vector machines, and K-nearest neighbors. Six out of ten input features including ammonium (NH4), total nitrogen (TN), hydraulic loading rate (HLR), the filter height (i.e., Height), aeration mode (i.e., Aeration), and types of inlet feeding (i.e., Feeding) have posed pronounced influences on the ARR. The Cubist algorithm appears the most optimal model showing the lowest RMSE i.e., 0.974 and the highest R2 i.e., 0.957. The contribution of variables followed the order of NH4, HLR, TN, Aeration, Height and Feeding corresponding to 97, 93, 71, 49, 34, and 34%, respectively. The generalization ability to forecast ARR using testing data achieved the R2 of 0.970 and the RMSE of 1.140 g/m2 d, indicating that Cubist is a reliable tool for ARR prediction. User interface and web tool of final predictive model are provided to facilitate the application for designing and developing SCW system in real practice

A TRM-based compatible strengthening solution for rammed earth heritage: investigation of the bond behavior

Raw earth is among the most ancient building materials and the related building techniques are found widespread around the world. Currently, it is estimated that about 25% of the global population lives in earthen buildings and about 10% of the UNESCO World Heritage is built with earth. Nevertheless, an important overlap can be observed when the geographical distribution of raw earth constructions is compared with that of the seismic hazard. This circumstance, combined with the seismic vulnerability of earthen buildings, results in a high seismic risk, as demonstrated by recent moderate earthquakes. Despite the current awareness for this problem, little has been done so far to develop proper strengthening solutions for the rammed earth heritage. Based on the effectiveness of externally bonded fibers for masonry buildings, the strengthening of rammed earth walls with an earth mortar coating reinforced with a geomesh is here adopted as a compatible solution. The objective of this work is to investigate and characterize the bond behavior of the above mentioned strengthening solution to further describe the response of the interaction mortar-mesh. To this purpose, an experimental program was undertaken based on a series of pull-out tests. Specimens were prepared using earth mortar, two different types of meshes (glass fiber and nylon) and considering different bonded lengths. The results highlighted distinct bond behaviors. In the case of the glass fiber mesh, the bond was granted by friction and mechanical interlocking, while the mechanical anchorage promoted by the transversal yarns granted the bond of the nylon mesh.FCT - Fuel Cell Technologies Program(SafEarth - PTDC/ ECM-EST/2777/2014)This work was partly financed by FEDER funds through the Operational Programme Competitiveness Factors (COMPETE 2020) and by national funds through the Foundation for Science and Technology (FCT) within the scope of project SafEarth - PTDC/ ECM-EST/2777/2014 (POCI-01-0145-FEDER-016737). The support from grants SFRH/BD/ 131006/2017 and SFRH/BPD/97082/2013 is also acknowledge