To limit the cross-sectional size of concrete structures, high-strength, lightweight concrete is preferred for the design and construction of structural elements. However, the main drawback of high-strength, lightweight concrete is its brittleness over normal-weight concrete. The ductility of concrete is a crucial factor, which plays an important role when the concrete structures are subjected to extreme situations, such as earthquakes and wind. This study aims to improve the ductility of high-strength, lightweight concrete by incorporating steel fibers. The palm oil clinker (POC)-based, high-strength, lightweight concrete specimens reinforced with steel fibers were prepared and their ductility was systematically examined. POC was used as aggregates and supplementary cementitious materials. Steel fibers from 0–1.50% (by volume), with an increment of 0.5%, were used in the concrete mix. Compression ductility, displacement ductility and energy ductility were used as indicators to evaluate the enhancement of ductility. Moreover, the compressive strength, flexural strength, stress-strain behavior, modulus of elasticity, load-displacement characteristics, energy absorption capacity and deformability of the concrete samples were investigated. The compression ductility, displacement ductility and energy ductility indexes were found to be increased by up to 472%, 140% and 568% compared to the control specimens (concrete with 0% steel fibers), respectively. Moreover, the deformability and energy absorption capacity of the concrete were increased by up to 566% and 125%, respectively. Therefore, POC-based, high-strength, fibrous, lightweight concrete could perform better than conventional concrete under extreme loading conditions as it showed significantly higher ductility.

One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 10⁶ m³, while the amount of water release based on the HA was 24.48 × 10⁶ m³. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.

Cement-based materials (CBMs) such as pastes, mortars and concretes are the most frequently used building materials in the present construction industry. Cement hydration, along with the resulting compressive strength in these materials, is dependent on curing temperature, methods and duration. A concrete subjected to an initial higher curing temperature undergoes accelerated hydration by resulting in non-uniform scattering of the hydration products and consequently creating a great porosity at later ages. This phenomenon is called crossover effect (COE). The COE may occur even at early ages between seven to 10 days for Portland cements with various mineral compositions. Compressive strength and other mechanical properties are important for the long life of concrete structures, so any reduction in these properties is of great concern to engineers. This study aims to review existing information on COE phenomenon in CBMs and provide recommendations for future research.

Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. Empirical heterogeneity within watersheds and limitations inherent to each estimation methodology pose challenges in effectively measuring and appraising hydrological statistical frameworks of spatial and temporal variables. This study emphasizes streamflow forecasting in the region of Johor, a coastal state in Peninsular Malaysia, utilizing a 28-year streamflow-pattern dataset from Malaysia’s Department of Irrigation and Drainage for the Johor River and its tropical rainforest environment. For this dataset, wavelet transformation significantly improves the resolution of lag noise when historical streamflow data are used as lagged input variables, producing a 6% reduction in the root-mean-square error. A comparative analysis of convolutional neural networks and artificial neural networks reveals these models’ distinct behavioral patterns. Convolutional neural networks exhibit lower stochasticity than artificial neural networks when dealing with complex time series data and with data transformed into a format suitable for modeling. However, convolutional neural networks may suffer from overfitting, particularly in cases in which the structure of the time series is overly simplified. Using Bayesian neural networks, we modeled network weights and biases as probability distributions to assess aleatoric and epistemic variability, employing Markov chain Monte Carlo and bootstrap resampling techniques. This modeling allowed us to quantify uncertainty, providing confidence intervals and metrics for a robust quantitative assessment of model prediction variability.

The deterministic approach, which utilizes the gradient information in the search process, is prone to trapping at local minima, primarily due to the presence of saddle points and local minima in the non-convex objective function of an artificial neural network (ANN). This study investigated the efficacy of a hybrid model that adopted a meta-heuristic algorithm (MHA) as an optimizer to extend the training ANN method, from a gradient-based to a stochastic population-based approach for streamflow forecasting. In the latter, parameter tuning utilizing the design of experiment (DOE) technique, has become an integral element in the optimization process due to reliance on their parameters. For model convenience, a wavelet transform was employed to decompose the series into sub-series. The empirical studies of MHA performance showed that the hybrid MHA-ANN was superior for streamflow forecasting, especially with the firefly algorithm that had an average RMSE = 96.06, an improvement of approximately 17% over the gradient-based ANN (RMSE = 113.92). However, among the adopted MHAs, not all are compatible with optimizing the ANN for streamflow forecasting, thus requiring a thorough study as performance varies from case to case. Two additional statistical tests, such as the Kruskal-Wallis H test and the Mann-Whitney U test, further validated such disparity in the MHA’s performance.

Using ceramic tile waste as a soil stabiliser in road construction is a potential solution to dispose of the waste material while providing a cost-effective alternative to traditional stabilising agents. The ceramic tile waste, when crushed and mixed with soil, helps to improve the strength and durability of the road base. However, the effectiveness of the ceramic tile waste as a soil stabiliser depends on the type and size of ceramic tiles used and the soil properties being stabilised. This study investigated the effect of ground tile waste on the plasticity, compatibility, and mechanical properties such as the unconfined compressive strength (UCS), indirect tensile test (IDT), flexural test (FS), and microstructural analysis. A range of soil mixtures was prepared by adding the different percentages of fine tile waste (TW): 5% to 40%. Including tile waste in the soil led to a decrease in its water-holding capacity, reducing the optimum moisture content required for optimal compaction. Meanwhile, the maximum dry density increased. The UCS, IDT, and FS improved when the optimum 15% of TW was used in the mixes. However, the strength decreased after 20% of the TW addition. This effect was particularly pronounced in the presence of excessive TW contents in soil samples without a pozzolanic reaction. Reusing tile waste as a soil stabiliser can significantly reduce the costs of purchasing new materials and helps to conserve natural resources and reduce the environmental impact of waste disposal.