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The present study aimed to investigate the effect of an expandable additive on the compressive strength and linear expansion of geopolymer cement, which is an alternative to ordinary Portland cement, for oil-well cementing. Fly-ash-based geopolymer cement samples, with the addition of slag cement as a strength enhancer, were prepared by using an elastomeric expandable additive (R-additive), which consists of styrene–butadiene rubber with a specific gravity of 0.945, at concentrations of 10%, 15%, 20% and 25% by weight of the solid blend, and cured in a water bath at 60 °C and atmospheric pressure, and a curing chamber at 90 °C and 3000 psi, or approximately 20.68 MPa. Mixability, amount of free water and slurry density were studied, and the effects of the concentration of R-additive on the compressive strength (F) and linear expansion (∆l/l0) of the samples were analyzed. When cured at 60 °C and atmospheric pressure, the highest F of 15.01 MPa was obtained when the concentration of R-additive was 10%, while the highest ∆l/l0 of 0.9985% was obtained when the concentration of R-additive was 25%. An increase in the curing temperature and pressure to 90 °C and 3000 psi (≈20.68 MPa) resulted in the reduction of F from 15.01 to 14.62 MPa and from 10.33 to 9.61 MPa, and the increase in ∆l/l0 from 0.52% to 0.63%, and from 0.99% to 1.32%, when the concentrations of R-additive were 10% and 25%, respectively. The findings suggest that the formulations adopted, which contain R-additive at concentrations ranging from 10% to 25%, fulfilled the requirements of the oil and gas industry.

This experimental study investigated the effects of polyvinyl alcohol (PVA) and copper-coated steel (CCS) on the mechanical properties and the post cracking behavior of fiber reinforced concrete (FRC). In designing high-performance concrete mixes, cement replacement materials are the essential ingredients. Therefore, the research objective was to investigate PVA and CCS fiber’s post-cracking performance in 100% cement concrete and concrete with 80% cement and 20% fly ash. The fiber content was fixed as a 0.3% volumetric fraction. CSS fibers required 15% more superplasticizer to achieve the desired slump of fresh concrete than the PVA fibers. Simultaneously, CCS fibers showed a 10% higher compressive strength than the concrete made of PVA fibers. Both fibers exhibited a similar effect in developing tensile and flexural strength. PVA fibers showed a value of 47 Gpa of secant modulus, and CCS fibers resulted in 37 Gpa in 100% cement concrete. In post-cracking behavior, CCS fibers showed better performance than the PVA fibers. The reason for this is that CCS showed 2.3 times the tensile strength of the PVA fibers. In comparing the two concretes, fly ash concrete showed about 10% higher compressive strength at 56 days and about 6% higher tensile and flexural strength. Similarly, fly ash concrete showed more than 15% first crack strength and flexural toughness than the 100% cement concrete in post-cracking behavior. Fiber-reinforced concrete containing PVA or CCS fibers showed enhanced post-cracking characteristics and its use could be preferred in structural applications.

The volcanic landforms associated with fluvial topography in the Hantangang River Volcanic Field (HRVF) have geoheritage value. The Hantangang basalt geological landform stretches along 110 km of the paleoriver channel of the Hantangang River. Since the eruption that formed this basalt occurred from 0.15 to 0.51 Ma, estimating the eruption in the HRVF that originated from two source vents in North Korea (Orisan Mountain and the 680 m peak) is challenging due to the limited recorded data for this eruption. In this study, we estimated this prehistorical eruption using 3D printing of a terrain model and Q-LavHA simulation. The results from the experiment were further analyzed using findings from an artificial neural network (ANN) and support vector machine (SVM) to classify the experimental lava area. The SVM classification results showed higher accuracy and efficiency in the computational process than the ANN algorithm. Results from the single eruptive vent scenario showed that the experiment had a higher accuracy than the Q-LavHA simulation. Further analysis of multiple vent scenarios in the Q-LavHA simulation has improved the accuracy compared with the single eruptive vent scenarios.

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.