A finite-element (FE) pullout model was developed and validated by an earlier experimental pullout test of cold-drawn shape memory alloy crimped fibre. Then, how the dimensions of crimped fibre, such as diameter, wavelength, embedded wave and wave depth, influence its pullout behaviour was investigated. The FE results indicate that the fibre diameter must be large enough to produce a good flexural rigidity. The recommended diameter is ∼1.0 mm. However, it should be noted that the pullout resistance is not improved much for thicker fibre. A short wavelength is more effective than a long one due to a higher maximum pullout stress and more peak points. More embedded waves produce a higher pullout resistance; however, the increasing pullout resistance slows down when the maximum pullout stress exceeds the yield strength. A five-embedded wave is proposed to produce a reasonable pullout resistance while making efficient use of materials. Finally, deep-crimped fibre generates a higher pullout resistance compared with shallow-crimped fibre. On the basis of the effect of the dimensions on the pullout behaviour, an optimum dimension concept is proposed for crimped fibre.

As built-environment carbon research expands from quantification toward mitigation decision support, a central unresolved question is whether reported carbon advantages remain valid under the conditions governing real decisions and implementation. This review integrates bibliometric mapping and structured full-text synthesis of 112 studies on buildings and road-transport infrastructure to identify systemic gaps across operational emissions, embodied carbon, and life cycle assessment. Reported outcomes were consistently conditioned by system boundaries, functional units, service life, spatial scale, data provenance, background energy and material systems, and implementation constraints. Interventions can also redistribute rather than simply eliminate emissions across lifecycle stages, supply chains, maintenance cycles, and time; consequently, neither process-level reductions nor nominal technology labels reliably indicate whole-system decarbonization without considering the configuration through which emissions are changed. Across the literature, analytical capability has advanced faster than evidence linking modeled mitigation potential to feasible implementation and verified reduction. Prediction, scenario analysis, optimization, and digital tools strengthen assessment and decision support but might not by themselves establish causal effectiveness, feasibility, or persistent mitigation. The review, therefore, reframes built-environment decarbonization from identifying universally superior technologies toward determining the conditions under which carbon evidence remains decision-valid. Future progress requires stage-adaptive information, decision-sensitive uncertainty analysis, feasibility-aware intervention selection, and stronger post-decision verification to preserve evidence integrity from assessment through implementation.