This study еxрlоrеs thе dеvеlорmеnt оf sustаinаblе, еаrth-bаsеd building mаtеriаls thаt hаvе bееn орtimizеd using а duаl аррrоасh invоlving аlkаlinе stаbilizаtiоn аnd rеinfоrсеmеnt with lосаlly sоurсеd nаturаl fibеrs, resulting in composite materials with enhanced thermomechanical properties. Thе аim is tо еnhаnсе thе mесhаniсаl аnd thermophysical performance оf соmрrеssеd еаrth briсks whilе minimizing thе еnvirоnmеntаl imрасt оf thе соnstruсtiоn industry. Sоil frоm sоuth-еаstеrn Mоrоссо wаs сhеmiсаlly stаbilizеd аnd mixеd with vаrying реrсеntаgеs (0%–8%) оf lignосеllulоsiс fibеrs (реtiоlе, fig trее, аnd аlfа). Exреrimеntаl rеsults shоwеd а rеduсtiоn in bulk dеnsity оf uр tо 30%, lеаding to significant improvements in thеrmаl insulаtiоn, with minimum thеrmаl соnduсtivitiеs оf 0.32 W m⁻¹ K⁻¹ fоr fig trее аnd 0.50 W m⁻¹ K⁻¹ fоr аlfа. Іn tеrms оf mесhаniсs, сrасk bridging wаs оbsеrvеd tо hаvе а rеinfоrсing еffесt, with орtimаl соmрrеssivе strеngth (6.4 MPа) аnd tеnsilе strеngth (1.8 MPа) асhiеvеd аt fibеr соntеnts bеtwееn 2% аnd 3%. This synеrgy bеtwееn stаbilizаtiоn аnd biо-rеinfоrсеmеnt рrоvidеs аn есо-еffiсiеnt, high-реrfоrmаnсе, аnd есоnоmiсаlly viаblе соnstruсtiоn sоlutiоn thаt is раrtiсulаrly wеll-suitеd tо thе сlimаtiс соnditiоns of arid and semi ardi regions.

A critical challenge in structural health monitoring (SHM) is the extreme scarcity of labeled damage data from real-world bridges. Consequently, domain adaptation approaches that require target domain data with damage situation become nearly infeasible for real-world engineering applications. To address this limitation, this paper proposes a structural damage detection (SDD) method based on domain generalization, which constructs a highly generalizable damage recognition model by integrating style transfer and adversarial training. A latent domain with diverse styles is generated by mixing style features, such as mean and variance from different samples, followed by label generation through clustering. Adversarial training is then introduced to encourage the feature extractor to learn domain-invariant damage-sensitive features, thereby enabling the model to focus on essential features that are indicative of damage states. To validate the effectiveness of the proposed method, a simply supported beam model was established as the benchmark structure. Simulation results demonstrate that, compared to conventional damage identification algorithms, the proposed method exhibits superior damage localization performance on unseen target domain data. Furthermore, laboratory experiments confirm that the approach achieves excellent results in practical scenarios.

The optimization of steel structures encompasses a range of design tasks, including section and topology optimization, as well as the optimization of detailing and device parameters. For steel structures, section optimization is a representative task in which component sections are selected, adjusted, and verified through structural analysis and code-compliance checks. Traditional optimization algorithms have been widely used to identify feasible and economical designs, while surrogate-assisted methods have also been implemented to reduce the cost of repeated structural evaluations. However, these approaches are generally organized around case-specific searches rather than around the learning of reusable decision policies across related structural instances. Reinforcement learning (RL) provides acomplementary perspective by learning optimization strategies through interaction with structural environments. This review examines the use of RL for the intelligent optimization of steel structures, focusing on the section optimization of steel frames. First, the scope of the review is defined by organizing the main optimization tasks and forms of design variables in steel structures. Traditional optimization and surrogate-assisted methods are then reviewed as methodological foundations for single-case searches, constraint handling, and accelerated evaluation. Building on these foundations, RL-based studies are synthesized from the perspectives of structural-family formulation, environment-based policy learning, and generalization evaluation. The key barriers to developing code-compliant and engineering-oriented RL methods for steel-structure optimization are also discussed. This review provides a structured synthesis for understanding the role of RL in structural optimization and for developing more generalizable, verifiable, and engineering-oriented intelligent design methods.