Evaluation of properties and performance of rubber-modified concrete for recycling of waste scrap tire

Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression

Compared environmental and economic impact from cradle to gate of concrete with natural and recycled coarse aggregates

Mechanical performance of roller compacted concrete pavement containing crumb rubber and nano silica

Effect of pumice powder and nano-clay on the strength and permeability of fiber-reinforced pervious concrete incorporating recycled concrete aggregate

Mechanical behavior of self-compacting concrete pavements incorporating recycled tire rubber crumb and reinforced with polypropylene fiber

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments.

Saltmarshes, known to be ecologically sensitive areas, face disturbances such as vegetation dieback due to anthropogenic activities such as construction. The current construction specifications recommended by state highway agencies do not specifically require documenting or restoring any prior saltmarsh soil/interstitial water properties, nor do they require re-establishing saltmarsh vegetation; restoring the abiotic properties and appropriate vegetation would enhance the long-term functionality and ecology of a disturbed area. In order to have a successful restoration of disturbed saltmarshes with healthy vegetation, the relationship between vegetative species and the properties of saltmarsh soils and interstitial water must be fully understood. In this study, field and laboratory tests were conducted for the soil samples from eight different saltmarsh sites in the Southeastern US Atlantic coastal region, followed by the development of a random forest model; the aim is to identify correlation among saltmarsh predominant vegetation types, redox potential, and salinity. The results reveal that moisture content and sand content are two main drivers for the bulk density of saltmarsh soils, which directly affect plant growth and likely root development. Moreover, it is concluded that deploying modern machine learning algorithms, such as random forest, can help to identify desirable saltmarsh soil/water properties for re-establishing vegetative cover with the reduced time after construction activities.

https://www.mdpi.com/2412-3811/6/5/70/pdf

Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia

Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands.

worldwide. The modulus can also be indirectly calculated by the results of other available devices. The Dynamic Cone Penetrometer (DCP) is an easy-to-access device for evaluating the strength of unbound pavement layers in a cheap, easy and fast way. In this study, the stiffness modulus of PFWD and penetration rate of DCP was correlated for the sub-grades ranging from well-graded sand (SW), which is highly consisted of SiO2 and Al2O3, and well-graded gravel (GW) classification. In addition, the results indicated that a good correlation exists between PFWD moduli and DCP results and also for the precise evaluation of the SW soil modulus with a considerable amount of SiO2 and Al2O3, a coefficient factor, of 2.39 C , was presented.

http://www.ijte.ir/article_14772_e057892efaaa3319521748ca76c12ae3.pdf

Iman Salehi Hikouei

Papers

Mechanical behavior of self-compacting concrete pavements incorporating recycled tire rubber crumb and reinforced with polypropylene fiber

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments.

Use of Random Forest Model to Identify the Relationships among Vegetative Species, Salt Marsh Soil Properties, and Interstitial Water along the Atlantic Coast of Georgia

Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands.

Evaluation of PFWD and DCP as quality control tools for sub-grade of GW and SW soils