Ultra-high-performance concrete (UHPC) is widely used in the field of large-span and ultra-high-rise buildings due to its advantages such as ultra-high strength and durability. However, the large amount of cementitious materials used results in the cost and carbon emission of UHPC being much higher than that of ordinary concrete, limiting the wide application of UHPC. Therefore, optimizing the design of the UHPC mix proportion to meet the basic properties of UHPC with low carbon and low cost at the same time will help to realize the wide application of UHPC in various application scenarios. In this study, the basic properties of UHPC, including the compressive strength, flexural strength, fluidity, and shrinkage properties, were predicted by machine-learning algorithms. It is found that the XGBoost algorithm outperforms others in predicting basic properties, with MAPE lower than 5% and R² higher than 0.9 in four output properties. To evaluate the comprehensive performance of UHPC, a further analysis was conducted to calculate the cost- and carbon-emissions-per-unit volume for 50,000 UHPC random mixes. Combined with the analytical hierarchy process (AHP) model, the comprehensive performance of UHPC, including basic properties, cost-per-unit volume, and carbon-emissions-per-unit volume, was evaluated. This study proposes an optimized UHPC mix proportion, based on low-cost or low-carbon emission, oriented to comply with the excellent overall performance and obtain its corresponding various properties.

To alleviate the workload in prevailing expert-based onsite inspection, a vision-based method using state-of-the-art deep learning architectures is proposed to automatically detect ceiling damage in large-span structures. The dataset consists of 914 images collected by the Kawaguchi Lab since 1995 with over 7000 learnable damages in the ceilings and is categorized into four typical damage forms (peelings, cracks, distortions, and fall-offs). Twelve detection models are established, trained, and compared by variable hyperparameter analysis. The best performing model reaches a mean average precision (mAP) of 75.28%, which is considerably high for object detection. A comparative study indicates that the model is generally robust to the challenges in ceiling damage detection, including partial occlusion by visual obstructions, the extremely varied aspect ratios, small object detection, and multi-object detection. Another comparative study in the F1 score performance, which combines the precision and recall in to one single metric, shows that the model outperforms the CNN (convolutional neural networks) model using the Saliency-MAP method in our previous research to a remarkable extent. In the case of a large-area ratio with a non-ceiling region, the F1 score of these two models are 0.83 and 0.28, respectively. The findings of this study push automatic ceiling damage detection in large-span structures one step further.

In recent years, with the acceleration of urbanization and the continuous expansion of construction at all scales, the safety, durability, and sustainability of building structures have come to face multiple challenges

Multimodal image data have found widespread applications in visual-based building façade damage detection in recent years, offering comprehensive inspection of façade surfaces with the assistance of drones and infrared thermography. However, the comprehensive integration of such complementary data has been hindered by low levels of automation due to the absence of properly developed methods, resulting in high cost and low efficiency. Thus, this paper proposes an automatic end-to-end building façade damage detection method by integrating multimodal image registration, infrared–visible image fusion (IVIF), and damage segmentation. An infrared and visible image dataset consisting of 1761 pairs encompassing 4 main types of façade damage has been constructed for processing and training. A novel infrared–visible image registration method using main orientation assignment for feature point extraction is developed, reaching a high RMSE of 14.35 to align the multimodal images. Then, a deep learning-based infrared–visible image fusion (IVIF) network is trained to preserve damage characteristics between the modalities. For damage detection, a relatively high mean average precision (mAP) result of 85.4% is achieved by comparing four instance segmentation models, affirming the effective utilization of IVIF results.