Mamba meets crack segmentation

Cracks pose safety risks to infrastructure and cannot be overlooked. The prevailing structures in existing crack segmentation networks predominantly consist of CNNs or Transformers. However, CNNs exhibit a deficiency in global modeling capability, hindering the representation to entire crack features. Transformers can capture long-range dependencies but suffer from high and quadratic complexity. Recently, Mamba has garnered extensive attention due to its linear spatial and computational complexity and its powerful global perception. This study explores the representation capabilities of Mamba to crack features. Specifically, this paper uncovers the connection between Mamba and the attention mechanism, providing a profound insight, an attention perspective, into interpreting Mamba and devising a novel Mamba module following the principles of attention blocks, namely CrackMamba. We compare CrackMamba with the most prominent visual Mamba modules, Vim and Vmamba, on two datasets comprising asphalt pavement and concrete pavement cracks, and steel cracks, respectively. The quantitative results show that CrackMamba stands out as the sole Mamba block consistently enhancing the baseline model's performance across all evaluation measures, while reducing its parameters and computational costs. Moreover, this paper substantiates that Mamba can achieve global receptive fields through both theoretical analysis and visual interpretability. The discoveries of this study offer a dual contribution. First, as a plug-and-play and simple yet effective Mamba module, CrackMamba exhibits immense potential for integration into various crack segmentation models. Second, the proposed innovative Mamba design concept, integrating Mamba with the attention mechanism, holds significant reference value for all Mamba-based computer vision models, not limited to crack segmentation networks, as investigated in this study.

Generative Structural Design Integrating BIM and Diffusion Model

Intelligent structural design using AI can effectively reduce time overhead and increase efficiency. It has potential to become the new design paradigm in the future to assist and even replace engineers, and so it has become a research hotspot in the academic community. However, current methods have some limitations to be addressed, whether in terms of application scope, visual quality of generated results, or evaluation metrics of results. This study proposes a comprehensive solution. Firstly, we introduce building information modeling (BIM) into intelligent structural design and establishes a structural design pipeline integrating BIM and generative AI, which is a powerful supplement to the previous frameworks that only considered CAD drawings. In order to improve the perceptual quality and details of generations, this study makes 3 contributions. Firstly, in terms of generation framework, inspired by the process of human drawing, a novel 2-stage generation framework is proposed to replace the traditional end-to-end framework to reduce the generation difficulty for AI models. Secondly, in terms of generative AI tools adopted, diffusion models (DMs) are introduced to replace widely used generative adversarial network (GAN)-based models, and a novel physics-based conditional diffusion model (PCDM) is proposed to consider different design prerequisites. Thirdly, in terms of neural networks, an attention block (AB) consisting of a self-attention block (SAB) and a parallel cross-attention block (PCAB) is designed to facilitate cross-domain data fusion. The quantitative and qualitative results demonstrate the powerful generation and representation capabilities of PCDM. Necessary ablation studies are conducted to examine the validity of the methods. This study also shows that DMs have the potential to replace GANs and become the new benchmark for generative problems in civil engineering.

Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset

Cracks provide an essential indicator of infrastructure performance degradation, and achieving high-precision pixel-level crack segmentation is an issue of concern. Unlike the common research paradigms that adopt novel artificial intelligence (AI) methods directly, this paper examines the inherent characteristics of cracks so as to introduce boundary features into crack identification and then builds a boundary guidance crack segmentation model (BGCrack) with targeted structures and modules, including a high frequency module, global information modeling module, joint optimization module, etc. Extensive experimental results verify the feasibility of the proposed designs and the effectiveness of the edge information in improving segmentation results. In addition, considering that notable open-source datasets mainly consist of asphalt pavement cracks because of ease of access, there is no standard and widely recognized dataset yet for steel structures, one of the primary structural forms in civil infrastructure. This paper provides a steel crack dataset that establishes a unified and fair benchmark for the identification of steel cracks.