A well-designed engineered soil for bioretention is important as it ensures that pollutant removal requirements are met. This laboratory study investigated the nutrient removal efficiency of bioretention media enhanced with 10% (by volume) additives from various waste materials (cockle shell, newspaper, printed paper, coconut husk and tyre crumb) and planted with Red Hot Chinese Hibiscus (Hibiscus rosa-sinensis), a common landscape shrub in tropical countries. The results showed that media enhanced with shredded newspaper demonstrated a significant improvement in total nitrogen (TN) removal (80.4%), compared to standard bioretention media (57.5%) without compromising total suspended solids (TSS) and total phosphorus (TP) removal, when dosed with actual runoff. The thick root system and rapid growth rate of the plant was proven to contribute to TN removal. This study concluded that shredded newspaper can be a potential addition to enhance bioretention media performance in treating stormwater, especially nutrient rich runoff from mixed development areas.

The understanding of how the sediment deposit thickness influences the incipient motion characteristic is still lacking in the literature. Hence, the current study aims to determine the effect of sediment deposition thickness on the critical velocity for incipient motion. An incipient motion experiment was conducted in a rigid boundary rectangular flume of 0.6 m width with varying sediment deposition thickness. Findings from the experiment revealed that the densimetric Froude number has a logarithmic relationship with both the thickness ratios t_s/d and t_s/y₀ (t_s: sediment deposit thickness; d: grain size; y₀: normal flow depth). Multiple linear regression analysis was performed using the data from the current study to develop a new critical velocity equation by incorporating thickness ratios into the equation. The new equation can be used to predict critical velocity for incipient motion for both loose and rigid boundary conditions. The new critical velocity equation is an attempt toward unifying the equations for both rigid and loose boundary conditions.

The current study aims to verify the existing equations for incipient motion for a rigid rectangular channel. Data from experimental work on incipient motion from a rectangular flume with two different widths, namely 0.3 and 0.6 m, were compared with the critical velocity value predicted by the equations of Novak & Nalluri and El-Zaemey. The equation by El-Zaemey performed better with an average discrepancy ratio value of 1.06 compared with the equation by Novak & Nalluri with an average discrepancy ratio value of 0.87. However, as the sediment deposit thickness increased, the equation by El-Zaemey became less accurate. A plot on the Shields Diagram using the experimental data had shown the significant effect of the sediment deposit thickness where, as the deposit becomes thicker, the dimensionless shear stress θ value also increased. A new equation had been proposed by incorporating the sediment deposit thickness. The new equation gave improved prediction with an average discrepancy ratio value of 1.02.

The maintenance of pavements takes considerable time and poses a significant task, especially when it comes to detecting cracks at the pixel level. Due to the complexity of pavement conditions, such as road markings, shadows, and oil stains, deep learning techniques are still a challenge in automating crack detection. This paper presents a novel methodology termed as CrackHAM, which is an encoder-decoder network founded on the U-Net architecture. The primary objectives of CrackHAM are twofold: to achieve accurate and robust pavement crack detection while reducing the parameters of the network. Our study introduces two significant improvements to the existing neural network architecture, namely the phased multi-fusion module and the dual attention mechanisms. These improvements improve the process of defect extraction, resulting in an improved level of performance. Furthermore, a novel module named HASPP is devised to augment the network’s capacity to acquire more comprehensive receptive fields. In order to lower the number of network parameters, a technique is employed whereby only use half of the number of input channels and output channels in the VGG16 are utilized as U-Net encoder modules. The empirical findings demonstrate that in the Deepcrack, Crack500, and FIND public datasets, CrackHAM achieves superior segmentation performance compared to the FCN, Deeplabv3, Swin-Unet, and U-Net models while utilizing only one-third of the computational resources.