Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Attention recurrent residual U-Net for predicting pixel-level crack widths in concrete surfaces. Retrieved May 24, 2026, from https://4ort.xyz/entity/attention-recurrent-residual-u-net-for-predicting-pixel-level-crack-widths-in-concrete-surfaces
MLA“Attention recurrent residual U-Net for predicting pixel-level crack widths in concrete surfaces.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/attention-recurrent-residual-u-net-for-predicting-pixel-level-crack-widths-in-concrete-surfaces.
BibTeX@misc{4ortxyz_attention-recurrent-residual-u-net-for-predicting-pixel-level-crack-widths-in-concrete-surfaces_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Attention recurrent residual U-Net for predicting pixel-level crack widths in concrete surfaces}}, year = {2026}, url = {https://4ort.xyz/entity/attention-recurrent-residual-u-net-for-predicting-pixel-level-crack-widths-in-concrete-surfaces}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Attention recurrent residual U-Net for predicting pixel-level crack widths in concrete surfaces — https://4ort.xyz/entity/attention-recurrent-residual-u-net-for-predicting-pixel-level-crack-widths-in-concrete-surfaces (retrieved 2026-05-24)
