This study investigates the effectiveness of deep soil mixing (DSM) in enhancing the strength and modulus of organic soils. The research evaluates how varying cement types, binder dosages, water-to-cement (w/c) ratios, and curing durations affect the mechanical properties of two different organic soils that were used; natural soil from the Golden Horn region of Istanbul with 12.4% organic content, and an artificial soil created from a 50/50 mixture of Kaolin clay and Leonardite, which has an acidic pH due to high organic content. The specimens were cured for four durations, ranging from seven days to one year. The testing program included mechanical testing; Unconfined Compression Tests (UCS), Ultrasonic Pulse Velocity (UPV) measurements, and chemical analyses; XRay Fluorescence (XRF) and Thermogravimetric analyses (TGA). The UCS tests indicated that higher binder dosages and extended curing durations significantly improved the strength. Higher w/c ratios resulted in decreased strength. Long curing durations resulted in strength values which were four times the 28-day strength values. This amplified effect of strength gain in longer durations was evaluated through Curing time effect index, (fc). The results were presented in terms of cement dosage effect, effect of cement type, effect of total water/cement ratio (wt/c), standard deviation values, E50 values and curing time effect index (fc) values respectively. Results of UPV tests were used to develop correlations between strength and ultrasonic pulse velocities. Quantitative evaluations were made using the results of XRF and TGA analyses and strength. Significant amount of data was produced both in terms of mechanical of chemical analyses.

Escalating usage of non-degradable plastics is raising significant concern. The search for bio-based degradable alternatives commenced far back, and the burgeoning progress in the development of bioplastics is featured as a critical solution to ongoing plastic pollution. Bioplastics are becoming a promising substitute for petroleum-based plastics, depending on the production source and post-use disposal management. Among all the promising materials, microbially produced polyester and polyhydroxybutyrate (PHB) belong to the polyhydroxyalkanoate (PHA) family and are biocompatible and non-toxic. PHB has remarkable thermal and mechanical properties, making it a potential replacement for ubiquitous plastics. In this study, PHB-producing bacteria were isolated from mangrove soil and checked for PHB accumulation using preliminary and confirmatory staining. Out of a total 25 isolates, 13 were found positive for PHB accumulation. Dairy wastewater was used as a cultivation medium for PHB production; the potential PHB-producing strain was selected for morphological and biochemical characterization up to the genus level and was found to be Bacillus sp (3.6 +/- 0.15g/L). Extracted PHB was characterized using FTIR, XRD, and TGA; in FTIR, the characteristic peak was recorded at 1724 cm-1, and XRD showed the crystallinity of PHB. outcome of the present study shows that dairy wastewater is an indispensable medium for PHB production in an eco-friendly way.

The problem of white pollution caused by waste agricultural mulch film (WAMF) has a long history and has brought great damage to the soil and ecological environment. The recycled WAMF has no processing performance because it is doped with a large amount of cotton straw and soil inorganic particles. In this study, it was reported for the first time that high-quality and efficient recovery of WAMF was carried out by means of solid-state shear milling ((SM)-M-3) technology. After the pretreatment of (SM)-M-3, the recycled WAMF is transformed into an active composite powder with a particle size of microns, which regains certain processing performance. Then we prepared a composite material similar to WPC (wood-plastic composite) by using the composite powder. It was found that under the action of strong three-dimensional shear force, the phase domain size of the composite decreased significantly, and the compatibility of each component improved. The macroscopic performance was that the tensile strength was increased by 65% and the bending strength was increased by 74%, reaching 8.30 and 17 MPa, respectively. The 24-h water absorption of this composite decreased by 13%. More importantly, the thermal stability was not significantly reduced during the milling process. This process does not require sorting, cleaning, or other operations, which can greatly simplify the process flow and improve recovery efficiency. It provides an effective solution to the problem of white pollution caused by WAMF.

In the present work, we report the effect of low-temperature plasma treatment on thermal, mechanical, and biodegradable properties of polymer composite blown films prepared from carp fish scale powder (CFSP) and linear low-density polyethylene (LLDPE). The CFSP was melt compounded with LLDPE using a filament extruder to prepare 1, 2, and 3 wt.% of CFSP in LLDPE polymer composite filaments. These filaments were further pelletized and extruded into blown films. The blown films extruded with 1, 2, and 3 wt.% of CFSP in LLDPE were tested for thermal and mechanical properties. It was observed that the tensile strength decreased with the increased loading content of CFSP, and 1% CFSP/LLDPE exhibited the highest tensile strength. To study the effect of low-temperature plasma treatment, 1% CFSP/LLDP polymer composite with high tensile strength was plasma treated with O2 and SF6 gas before blow film extrusion. The 1% CFSP/LLDPE/SF6-extruded blown films showed increased thermal decomposition, crystallinity, tensile strength, and modulus. This may be due to the effect of crosslinking by the plasma treatment. The maximum thermal decomposition rate, crystallinity %, tensile strength, and modulus obtained for 1% CFSP/LLDPE/SF6 film were 500.02 degrees C, 35.79, 6.32 MPa, and 0.023 GPa, respectively. Furthermore, the biodegradability study on CFSP/LLDPE films buried in natural soil for 90 days was analyzed using x-ray fluorescence. The study showed an increase in phosphorus and calcium mass percent in the soil. This is due to the decomposition of the hydroxyapatite present in the CFSP/LLDPE biocomposite. Schematic diagram of polymer film fabrication process. image

The shear wave velocity is among the key parameters that are responsible for damage caused during an earthquake. Determining shear wave velocity is a costly and time-consuming direct geophysical method. In the present paper, a machine learning model has been developed to obtain the subsurface shear wave velocity profile without using direct methods. The bore log information and the subsurface shear wave velocity profile available at various stations of Japan's Kyoshin network (K-NET) have been utilized for training this machine learning model. The parametric correlation study indicates that simple parameters like rock/soil type, the thickness of the layer in the model, and standard penetration test (SPT-N) value directly correlate with the medium's shear wave velocity. A stacked ensemble machine learning model named VelProfES (an acronym for Velocity Profiler using Ensemble machine learning models) has been developed in this paper and has been utilized for effective prediction of the shear wave velocity profile using basic information from borelog data. The dataset used in the training and testing of the machine learning model consists of borelog and shear wave velocity information from 1101 stations. Of 1101 stations, 657, 283, and 71 stations have been utilized for training, testing, and validating the machine learning model. Training, testing, and validation of the developed machine learning model consist of parameters from 12351, 5279, and 1388 velocity layers. The problem of data imbalance based on soil type has been addressed using an additional 10510 layers of synthetic borelog data generated from conditional generative adversarial networks (CTGANs). A feature and model ablation study was conducted for the VelProfES model to provide substantiation for the model and feature selection choices. The predicted shear wave velocity profiles were compared at specific stations, focusing on average velocities at 5, 10, 15, and 20 depths. Further, the predicted values have been compared with the empirical relation of Sil and Haloi (2017) and a trained polynomial model. The machine learning model demonstrates close alignment between predicted and actual values across a broad spectrum of velocities, a contrast not observed in the empirical relation and polynomial model. The results show that the machine learning models and augmented data generated using CTGANs can efficiently minimize the error between actual and predicted subsurface shear wave velocity values.

Tree fall onto railway lines puts passengers at risk and causes large economic losses due to disruption of train services and damage to infrastructure. Railway lines in Germany are vulnerable to tree fall because of the large number of trackside trees that exist in that country with approximately 70% of all railway lines being tree-lined. In this paper we first tested whether a hybrid-mechanistic tree wind damage model, ForestGALES, could identify the sections of the railway network affected by tree fall in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH). We secondly tested whether the model, in combination with meteorological forecast models, could predict where tree fall occurred during a damaging windstorm. We used information on tree characteristics derived from LiDAR and aerial photography along the railway line network in NRW and TH to calculate the critical wind speed (CWS) at which damage is expected to happen for every individual tree as a function of its size and species, and the underlying soil. The railway network was then divided into 500 m sections and the statistics of the CWS, tree height, and species composition (broadleaf/conifer mix) within each were calculated. Analysis of past tree fall events recorded by Deutsche Bahn AG (DB) showed that there was a significantly lower minimum CWS and significantly greater maximum tree height in sections that had recorded damage. In a second step we compared the calculated CWS values for all trees against downscaled wind speed assessments across the two federal states during Storm Friederike (named Storm David internationally) on 18 January 2018 and tested the ability of the model to discriminate sections with recorded damage during the storm. Excellent model discrimination was found with an AUC value of 0.82 and an overall model accuracy of 74.2%. The first test showed that the ForestGALES model with precise individual tree information can identify the sections of a railway network most vulnerable to tree fall. The second analysis showed, for the one storm tested, that the ForestGALES model when combined with predicted storm wind speeds can identify the most probable sections of the railway network to experience tree fall during an approaching damaging storm. Such information could be of value in firstly planning remedial work along railway lines, and secondly preparing the railway network ahead of a major storm.