Construction and demolition (C&D) waste has become a research hotspot due to the need for environmental sustainability and strength enhancement of cementitious materials. However, wider applications of C&D waste are limited, as its non-homogeneous surface nature limits its workability. This research evaluated the feasible utilization of C&D waste as aggregates in polypropylene-fiber-reinforced cement-stabilized soil (CSS) under sulfate-alkali activation. CSS specimens incorporated Portland cement and C&D waste in 10%, 20%, and 30% proportions. Also, polypropylene fiber after alkali activation by sodium sulfate (at 0.2%, 0.4%, and 0.8% dosing level) was defined as 1%, 2%, and 4%. Strength enhancement was examined through unconfined compressive strength (UCS) and flexural strength tests at 7, 14 and 28 days. Test results indicated that mechanical properties showed significant improvement with increasing levels of Portland cement and sodium sulfate, while the improvement dropped after excessive addition of C&D waste and polypropylene fiber. Optimal proportioning was determined as 30%, 4%, 20%, and 0.8% for Portland cement, polypropylene fiber, C&D waste, and sodium sulfate, respectively. Scanning electron microscope (SEM) analysis attributed the enhancement to hydration product (ettringite) formation, bridging effect and increased particle friction. Additionally, the decrease in amplification was ascribed to the destruction of interface transition-zone (ITZ) strength, resulting in premature failure.

Three-dimensional (3D) concrete printing (3DCP) technology attracts significant attention from research and industry. Moreover, adequate mechanical performance is one of the primary properties for materials, meeting the demand of structural safety using 3DCP technology. However, research on curing conditions as the significant influence factor of mechanical capacity is required to accelerate the practical application of 3DCP technology. This study aims to explore the impact of various steam curing conditions (heating rate, constant temperature time, and constant temperature) on the mechanical performance of printed concrete containing solid wastes. Moreover, the optimal steam curing conditions are obtained for compressive, tensile, and flexural properties in different directions. Subsequently, anisotropies in the mechanical properties of printed composites and interlayer bonding behaviors are investigated when various curing conditions are employed. The result shows that steam curing conditions and solid waste incorporation improves the interlayer bond for 3D printed cement-based composites.

The process of image collection of high-altitude rock cracks using unmanned aerial vehicle (UAV) suffers from insufficient resolution and motion blur, which prevents more accurate detection of micro-cracks. Therefore, in this study, a rock crack refinement detection process (RC-RDP) based on super-resolution reconstruction (SRR) technique and semantic segmentation (SS) network is developed to detect micro-cracks. Four SRR networks (RCAN, SRDenseNet, ESRGAN, BSRGAN) and six SS networks (PSPNet, SegNet, DeepLab V3+, UNet++, UNet++(CBAM), SegFormer) are trained and tested separately using rock crack datasets. SRR indicators (PSNR and SSIM) and SS indicators (Precision, Recall, F1-Score and IoU) are applied to evaluate the performance of SRR networks and SS networks, respectively. According to the evaluation indicators of each network performance, in this paper, the RCAN network (PSNR = 31.08 dB and SSIM = 88.56%) is applied in the SRR part, and the UNet++ (CBAM) network is used in the crack detection part (Precision = 0.874, Recall = 0.886, F1-Score = 0.879 and IoU = 0.785). In addition, a novel data acquisition process integrating skeletonization algorithm, feature nodes detection algorithm, normal vector estimation algorithm and width extraction algorithm is used to calculate the minimum width W_Min, maximum width W_Max and average width W_A information of the crack traces. The results of this paper show that the application of RC-RDP based on deep learning can obtain better super resolution (SR) images and more accurate width information. The method in this study shows excellent performance in detecting rock cracks.