This paper presents the findings of an investigation on the compressive strength properties of kenaf fiber composite mortar. Water–retted kenaf fibers were used as reinforcement in cement mortar. Fiber contents of 1%, 2% and 3 % (by weight of cement) with varying lengths of 10mm, 20mm and 30mm were used to produce 50mm mortar cubes. The Composite mortars were cured in water for 3, 7, 14, 21, and 28days. A total of 150 mortar cubes were used for the study. Density, water absorption and compressive strength tests were conducted on the composite mortar. Regression analysis was carried out on the compressive strength results using Minitab 15. The results showed that water absorption and density of the composite mortar increased as the volume of fiber and length increased. Compressive strength decreased with increasing fiber volume and length. However, there was increase in compressive strength of between 0.21%-22.3% for composite mortar containing 1-3% volume of fiber with 10mm fiber length. The fitted regression model showed a perfect relationship (R2=84.5%) between compressive strength, fiber volume, fiber length and curing age. Therefore, fiber volume, fiber length and curing are useful predictors of the selected model. Model adequacy test reveals that the fitted regression model is highly adequate. There was no statistically significant difference in the compressive strength of the control samples and those containing 1-3% fiber volumes with fiber length of 10mm.

Data augmentation is one of the effective solutions to improve the performance of machine learning models in general and deep learning in particular. Data augmentation techniques bring different effects to each model, but very few studies have considered this issue. This study investigated the effect of five distinct data augmentation strategies on a custom-built Convolutional Neural Network (CNN) and nine pre-trained CNN models for crack detection. All ten models were initially trained on a reference dataset of unaugmented images, followed by separate experiments using the augmented datasets. The results show that the pre-trained models, especially VGG-16, EfficientNet-B7, Xception, DenseNet-201, and EfficientNet-B0, consistently achieved greater than 98% in accuracy across all augmentation techniques. Meanwhile, the custom-built CNN was very sensitive to illumination changes and noise. Image rotation and cropping have minimal negative impact and sometimes improve performance. The findings demonstrate that combining data augmentation with state-of-the-art pre-trained models offers a powerful and efficient alternative to the reliance on large-scale datasets for accurate crack detection using CNNs.

Early damage detection in bridges is fundamental to their continued safety, and therefore of utmost importance to the bridge managers and policy makers. The traditional visual inspection which is the common practice for bridge inspection is inefficient, time-consuming, costly, risky, subjective and require the expertise of highly qualified inspectors. Consequently, the use of unmanned aerial systems (UASs) has gained significant attention in the area of bridge inspection. Most UAVs are quite expensive, ranging between $8000-$25000 for the best categories. It however requires the use of UAV equipped with high-cost sensors and longer battery duration, and adequate man power for real-time inspection. The expense of the UAV based inspection makes it unaffordable for many African countries. More recently, 3D models are increasingly deployed for image-based damage identification in bridges and other structures. The 3D models are constructed inform of a digital twin, and allow for computerized inspection using low-cost drones. This paper presents 3D Reconstruction approach for damage inspection and condition assessment of a bridge using images from low-cost unmanned aerial vehicle (UAV). The overall approach was illustrated in form of a case study on a precast concrete bridge. The 3D Reconstructed model of the bridge was virtually inspected to detect damages such as cracks, delamination, concrete deterioration, etc. The results showed that 3D reconstruction using low-cost UAV has great potential in its applications in bridge assessment.

Missing bolts compromise the structural integrity of bolted connections in steel bridges and industrial infrastructure. Manual visual inspection remains labour-intensive, subjective, and hazardous in hard-to-reach locations. This study presents a comparative benchmarking framework for unmanned aerial vehicles (UAVs) missing-bolt patch classification using convolutional neural network (CNN), focusing on balancing accuracy and computational efficiency. A UAV-acquired dataset of bolt-centric image patches was developed to evaluate four systematic experimental schemes: (i) a custom lightweight CNN trained from scratch, (ii) the lightweight CNN integrated with Squeeze-and-Excitation (SE) attention blocks across multiple positions, (iii) nine fine-tuned state-of-the-art (SOTA) pretrained CNN backbones, and (iv) SE-enhanced versions of these pretrained models. All architectures were evaluated under a standardised experimental protocol. Results show that the proposed lightweight CNN achieves classification performance comparable to heavyweight pretrained models while requiring significantly lower computational resources. Integrating SE blocks did not improve classification performance for this localised task and, in several configurations, reduced accuracy and training stability. Pretrained transfer learning models achieved high accuracy overall, but their computational complexity limits direct deployment on edge devices and UAV platforms. Grad-CAM visual explanations confirmed that the lightweight CNN consistently focuses on relevant bolt and hole regions. The findings demonstrate that a task-specific lightweight CNN offers a practical balance between inspection reliability and deployment efficiency for automated structural monitoring.