Editorial

Every year, I excitedly await the summer breeze, signalling the end of cold months. I watch with joy as my backyard bursts into bloom with Tulip colours, enjoy early morning exercise and trade in heavy snow boots for sandals. Recently, these all can be overshadowed by a single scroll on your phone, revealing the weather forecasts. Crazy changes, plummeting temperatures as if winter is reversing, unexpected rain in a dry season, all these drastic changes can drive one to madness. Unfortunately, this has become the new norm amidst the climate change crisis of our earth. These shifts in the average weather pattern from region to region are driven by human activities, particularly our heavy reliance on fossil fuels. The consequences are dire, including rising sea levels, heatwaves, droughts, floods, and changes in precipitation patterns. These changes threaten to erode the cultural heritage of our cities, representing an irreparable loss. An example is adobe buildings, built from earth and represent a model of green construction (Revuelta-Acosta et al., 2010). The required construction materials, such as soil, are not energy intensive, leading to approximately 370 GJ per year and reducing CO2 emission, making it an environmentally friendly building (Revuelta-Acosta et al., 2010; Shukla et al., 2009).After the earthquakes in 2017 in Mexico, the adequacy and safety of adobe-based constructions became a concern. A problematic choice arose between maintaining unique cultural and social facets; and attempting to reduce the risk for long-term sustainability. Hence, the first paper (Ramírez Eudave et al., 2024) of the current issue evaluated the mechanical characteristics of adobe samples from the state of Morelos. A series of experiments were conducted on 13 historical buildings. Initially, an ultrasonic pulse velocity testing technique was used to assess the variability of the adobe material present in those buildings. Another set of laboratory tests was carried out to determine the mechanical properties of some collected adobe units. The reported results provided valuable insights into the mechanical properties of the adobe and can be complementary to future experimental campaigns. However, the limited number of data highlighted the need for more advanced testing techniques to overcome sampling challenges when testing historical materials. This leads us to the second paper in the current edition.The second paper (Oommen and Philip, 2024) introduces a technique to evaluate timber moisture content (MC) using a transducer based on a spiral planar inter-digital capacitive structure. The sensor capacitance measured depends strongly on the material dielectric properties and is very sensitive to MC changes. The MCs in varieties of timber specimens were measured, showing good fitting with a correlation coefficient higher than 0.98 and a best sensitivity of 0.01 pF/MC%. The present inter-digital capacitive technique offers several advantages over existing moisture measurement methods for timber, including sensitivity, dynamic range, cost-effectiveness, non-invasiveness, non-destructiveness and measurement speed. However, the measurement sensitivity is dependent on the timber species, thus requiring auto-calibration to develop a commercial appliance for timber moisture measurement. It is possible to develop an artificial intelligence (AI) module. Implementing AI will have even more benefits, as illustrated in the third paper on the current issue.Artificial neural networks (ANN) are modelling decision-making systems featured with automated knowledge extraction and high inference accuracy (Yang and Yang, 2014). Thus, the third paper (Rashno et al., 2024) employed ANNs capabilities to predict the mechanical properties of fibre-reinforced ultra-high-performance self-compacting concrete (FRUHPSCC). A data set including garnet and basalt aggregates, nano-silica, fly ash, steel fibre, and other mixture components were used as inputs, while the compressive strength for all tested mixtures was set as the output. ANNs with five different training algorithms were applied to predict the compressive strength. This was followed by employing the grasshopper optimization algorithm (GOA) to optimize and hybridize the trained neural networks. Developed model showed a high prediction accuracy of the compressive strength of FRUHPSCC. The findings pave the way for a wider acceptance of ANNs as a sustainable practice to achieve durable and high-quality concrete while reducing its environmental impact. This supports UN SDGs and directly contributes to the development of sustainable cities and communities by achieving responsible consumption and production.Aligned with UN SDGs 12 targeting sustainable consumption of materials, the fourth paper (Debnath et al., 2024) emphasizes the utilization of locally available aggregate as alternative construction materials. In this study, crushed over-burnt brick aggregate (Cobba) was used in pervious concrete (PC), a special type of concrete characterized by high porosity yet expected to possess some strength. The primary focus of investigation was the effect of the number of compaction blows on permeability and compressive strength. Generally, increasing number of blows resulted in higher compressive and split tensile strengths but significantly reduced permeability. The optimum number of compaction blows will vary and will depend mainly on the coarse aggregate size. The paper proposed some equations to estimate the pore parameters and strength of the PC mix. Additionally, the authors highlighted the importance of examining the effect of compaction on the clogging behaviour of such concrete mixtures. The last paper in the current issue delves further into the clogging behavior.The last paper (Nazeer et al., 2024) concentrates on assessing the clogging potential of PC for different cloggers, namely, sand, clay and their combination (S&C). Natural clogging conditions were simulated on cylindrical specimens with various sediment loads. Furthermore, the effectiveness of various rehabilitation techniques, such as vacuuming, pressure washing and vacuuming followed by pressure washing, on the recovery rate of infiltration was examined. The findings revealed that S&C clogging resulted in complete permeability loss after the fourth or fifth cycles due to the formation of a mud lid on the surface. However, clogging with clay was the worst due to its cohesive and sticking nature, which led to the formation of layered flow paths and permanent choking of pores. Vacuuming, followed by pressure washing, showed the best recovery rate (ranging between 64% and 78%). Moreover, it was emphasized that pressure washing alone must be avoided as it can cause an accumulation of sediment in the lower strata, inducing secondary clogging in PC.

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