Biopolymers are viewed as effective and eco-friendly agents in soil modification. This study focuses on the wettability analysis of polyacrylamide (PAM) solutions for soil remediation. The contact angle, surface tension, and viscosity of PAM solutions were experimentally evaluated in air- and decane-biopolymer solution systems. Furthermore, a micromodel was used to investigate the pore-scale displacement phenomena during the injection of the PAM solution in decane and or air saturated pores. The contact angle of the PAM solution linearly increases with increasing concentration in air but not in decane. The surface tension between the PAM solution and air decreases at increasing concentration. The viscosity of the PAM solution is highly dependent on the concentration of the solution, shear rate, and temperature. Low flow rate and low concentration result in a low displacement ratio level, which is defined as the volume ratio between the injected and the defended fluids in the pores. The displacement ratio is higher for PAM solutions than distilled water; however, a higher concentration does not necessarily guarantees a higher displacement ratio. Soil remediation could be conducted cost-efficiently at high flow rates but with moderate concentration levels.

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties and complicated underground geometries. As a simplified approach, the one-dimensional wave propagation model, which assumes that seismic waves travel vertically through a horizontally layered medium, is widely adopted for its reasonable performance in many practical applications. This study explores the potential of sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, and transformers, as an alternative for seismic ground response modeling. Utilizing ground motion data from the Kiban Kyoshin Network (KiK-net), we train these models to predict ground surface acceleration response spectra based on bedrock motions. The performance of the data-driven models is compared with the conventional equivalent-linear analysis model, SHAKE2000. The results demonstrate that the deep learning models outperform the physics-based model across various sites, with the transformer model exhibiting the smallest average prediction error due to its ability to capture long-range dependencies. The 1D-CNN model also shows a promising performance, albeit with occasional higher errors than the other models. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. These findings highlight the potential of sequence deep learning approaches for seismic ground response modeling.

The physical properties of granular materials (such as hydraulic, strength, and thermal properties) are largely dependent on their density (or porosity) and particle size distribution. In infrastructure design, the thermal properties of soils are now more important than in the past. However, our understanding of the thermal properties of mixed granular materials is still poor. In this study, the thermal conductivity of silica sands with different porosities and particle sizes was experimentally investigated, based on ASTM D5334-14. The thermal conductivity of granular materials is presented as a function of the porosity and proportion of fine particles. The thermal conductivity tends to be low when the porosity is high and the proportion of fine particles is low (and vice versa). When the fine particles are small enough to fill the pore body of the larger particles, the coordination number increases; thus, the thermal conductivity increases when the proportion of fine particles is high. Therefore, both the porosity and particle size distribution should be carefully considered when the thermal conductivity of mixed silica sand is evaluated.

Porous asphalt has been used for permeable pavement to improve safety of roadways and the effectiveness of storm water management. As a surface drainage layer with frequent exposure to water, this material is affected by moisture. In this study, dynamic modulus tests were performed on both moisture unconditioned and conditioned specimens to characterize viscoelastic properties of porous asphalt mixture. The dynamic modulus values of porous asphalt materials with air void content of 9.0% and 20.5% were investigated at dry condition and after specified moisture conditioning cycles. One cycle of moisture conditioning procedure included placing specimens in water tank at 60 °C for 24 h, and then in another water tank at 25 °C for additional 2 h. The results showed that porous asphalt mixture with lower air void content resulted in higher values of dynamic modulus, and these values of porous asphalt with air void content of 9.0% was about 1.5 to 3.0 times that of porous asphalt with air void content of 20.5%. Higher value of the first number of performance graded binder (average 7-day maximum pavement design temperature) seems to make the dynamic modulus values at high temperatures larger. After moisture conditioning, the dynamic modulus of porous asphalt mixture increased, overall, especially at low temperatures. The appropriated selection of asphalt binder, a weakening of asphalt due to moisture damage can be reduced.

Open-graded aggregates (OGAs) are free-draining materials often used as the base layer of permeable pavements to allow the infiltration or drainage of stormwater. Despite their widespread use, the compaction quality of OGA base layers has not been specified properly. The currently used density-based compaction quality control (QC) has limitations; obtaining the field density and maximum dry density of OGAs by typical methods is challenging, due to their unique properties. To overcome these limitations, modulus-based compaction QC can be used as an alternative. In this study, five different OGAs were chosen and compacted into a specially built soil chamber to measure their densities. The light weight deflectometer (LWD) and the soil stiffness gauge (SSG) were used to evaluate the modulus of the compacted OGAs. The vibratory hammer compaction test was conducted to obtain the maximum dry density of the aggregates. Through these tests, the relationship between the modulus of the compacted aggregates and the relative density was obtained, and efforts to find a modulus range that ensures proper compaction were made. It was found that the LWD and SSG are valid and reliable devices for monitoring the modulus change of OGAs due to compaction.

The permeable pavement is one of Low Impact Development technics that allows stormwater to infiltrate through the pavement surface and the underlying base layer, thereby reducing surface runoff and preventing water contamination. For permeable base layers of permeable pavements, open-graded aggregates are often used to infiltrate and store stormwater in the pore of aggregate base layers. The mechanical behavior of open-graded aggregates has not been a major interest of pavement industry and society, and therefore there is much less information known for behavior of compacted open-graded aggregates comparing to dense-graded materials. This study aims at investigating the mechanical behavior of compacted permeable or open-graded aggregate base materials based on field experiments. Five different open-graded aggregates were selected, and they were compacted in the field up to 12 passes with a 10-ton vibratory compaction roller. The mechanical behaviors of aggregates were evaluated by conducting plate load tests at 2, 4, 8, and 12 passes of roller. For the test conditions considered herein, the strain modulus at the first loading seems to provide more consistent results with respect to aggregate types and level of compaction than other stiffness measures from plate load tests.