This investigative study aims to study the mechanical and morphological properties of fly ash (FA)-based geopolymer paste as a repair material when applied on ordinary Portland cement (OPC) overlay concrete. The first part of this study investigates the optimal mix design of FA-based geopolymer paste with various NaOH concentrations of 8, 10, 12, and 14 M, which were used later as a repair material. The second part studies the bonding strength using a slant shear test between the geopolymer repair material and OPC substrate concrete. The results showed that a shorter setting time corresponds to the higher NaOH molarity, within the range of 53 and 30 min at 8 and 14 M, respectively. The compressive strength of FA-based geopolymer paste was found to reach 92.5 MPa at 60 days. Also, from the slant shear test results, prism specimens with 125 mm length and 50 mm wide have a large bond strength of 11 MPa at 12 M. The scanning electron microscopy/energy-dispersive X-ray (SEM/EDX) analysis showed that the OPC substrate has a significant effect on slant shear bond strength, where the presence of free cations of Ca²⁺ on the OPC substrate surface contributed to the formation of calcium alumina-silicate hydrate gel (C-A-S-H) by building various cross-links of Ca-O-Si.

Drought analysis via the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) is necessary for effective water resource management in Sarawak, Malaysia. Rainfall is the best indicator of a drought, but the temperature is also significant because it controls evaporation and condensation. This study examined drought periods in the state of Sarawak using the SPI and SPEI based on monthly precipitation and temperature data from thirty-three rainfall stations during a forty-year period (1981–2020). This analysis of drought conditions revealed that both the SPI and SPEI were able to detect drought temporal variations with distinct time scales (3, 6, 9, and 12 months). Taking precipitation and evapotranspiration data into account, the SPEI was able to identify more severe-to-extreme drought in the study area over longer time periods and moderate droughts over shorter time periods than the standard drought index. According to Pearson correlation coefficients, a substantial association existed between the SPI and SPEI during hydrological dryness. Based on the results, the temperature is a decisive factor in drought classification, and the SPI should only be used in the absence of temperature data.

This paper presents a review on fly ash as prime materials used for geopolymer. Due to its advantages of abundant resources, less in cost, great workability and high physical properties which lead to achieve high mechanical properties. Fly ash is considered as one of the largest generated industrial solid wastes or so-called industrial by products, around the world particularly in China, India and USA. The characteristics of fly ash allow it to be a geotechnical material to produce geopolymer cement or concrete as an alternative of Ordinary Portland cement. Many efforts are made in this direction to formulate a suitable mix design of fly ash based-geopolymer by focusing on fly ash as the main prime material. The physical properties, chemical compositions and chemical activation of fly ash are analysed and evaluated in this review paper. Reference has been made to different ASTM, ACI standards and other researches work in geopolymer area.

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector (PRSC) is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression (SVR), artificial neural network (ANN), and M5 tree model. In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA‐DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). However, the other comparable models such as GA‐M5Tree, GA‐ANN, and GA‐SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model.