This research introduces an extensive database aggregating 54 experimental programs with 804 test specimens and 45 input parameters, investigating the implications of chloride-induced corrosion on the deteriorated mechanical response of corroded reinforced concrete beams. Several machine learning models are explored to determine the highest performing predictor for five key response variables – the residual ultimate moment capacity, residual capacity factor, yield load, yield displacement, and the ultimate displacement. Three existing analytical approaches are included for comparison to verify the efficacy of the trained statistical models. The optimized machine learning models significantly outperformed conventional analytical methods and achieved high levels of predictive accuracy. Ensemble tree-based learning algorithms, namely gradient-boosting regression trees and random forests, consistently produced the best predictions. Finally, the top-performing models are aggregated into a Python-based application that allows users to input new data and predict the mechanical response of a corroded beam failing in bending.

Building materials that are reused in a circular economy context are often non-standard because previous usage has altered their original properties. Material matching algorithms aid design and construction by matching components in stock with those needed in a design. The efficiency of these matches is assessed based on factors such as waste, logistics, and structural applicability. In this paper, we evaluate the efficacy and practicality for design evolution of construction-relevant optimisation goals. We evaluate goals based on their construction-time and design time results, using a software pipeline optimising both the mapping and design. One design was also constructed based on the optimised matching, to assess additional considerations when working component-specific designs. Based on difficulties and inefficiencies observed during fabrication, we propose two alternate matching strategies and compare their effects on the chosen factors. The study reveals that component allocation systems frequently overlook the need for both contingency planning and redundancy in the construction process. Additionally, inaccuracies in inventory tracking can significantly compromise the feasibility of the planned design. The stability of these design assistive techniques strongly affects their ability to be applied in large-volume projects. This implies the need for a new set of objective factors to be incorporated into existing methods of design optimisation.

Repointing is a prevalent maintenance practice in Northern Europe aimed at mitigating moisture-related damage in brick masonry buildings. Although commonly used, evidence of its effectiveness is limited. This study assesses repointing's role in reducing damage risks by conducting a probabilistic hygrothermal analysis of two wall types: timber frame walls and masonry cavity walls. Results indicate that repointing could reduce the mold index in timber frame walls and moisture content in the autoclaved aerated concrete (AAC) layer of masonry cavity walls, particularly in walls with visible defects. However, its impact is minimal on walls without significant deficiencies. Moreover, the study suggests that repointing, given its labor-intensive and costly nature, may not always be the most judicious maintenance strategy. It recommends a selective repointing approach, suiting the specific conditions and needs of the wall based on its location, orientation, and existing state, rather than a blanket application across all façade sections.

The vertical transmission of bioaerosols in building drainage systems is an understudied route of disease spread. This research examines how the position of drainage floors, flush volume, pipe type, and building story number affect the dynamics and reach of bioaerosols in such systems. Our findings show that drainage points positioned lower in a building amplify the number of the affected floors above, with recorded pressure differences at various drainage points ranging from 900 to 3200 Pa, depending on flush volume and floor position. The transition to a double pipe system significantly reduced pressure differences (to as low as 330 Pa in vent pipes), thereby diluting bioaerosol concentrations but increasing their dispersion across more floors. Building story number further influenced bioaerosol spread, with higher buildings exhibiting negligible pressure differences across stories (1400–2300 Pa) but a pronounced increase in affected floors above the drainage site.