Nowadays, the incorporation of natural fiber, such as coir fiber, to high-strength concrete has sparked a lot of attention in the construction materials industry. This is because coir fibers are significantly cheaper and more widely accessible than synthetic fibers. Natural fibers such as bamboo, flax, hemp, and coir have distinct microstructures and chemical compositions from cement-based materials. The physical and mechanical properties of natural fiber, such as coir fiber, are significantly correlated with fiber concentration and cellulose component. However, coir fiber has high stretching to failure, while bamboo, flax, and hemp fibers are very resistant to stress and increase stiffness. Based on these distinctive fiber qualities, it is anticipated that coir fiber would facilitate the development of cement-based materials for advanced concrete building applications. In this paper, coir fiber-reinforced cement-based concretes were evaluated in terms of workability, compressive strength, flexural strength, splitting tensile strength, modulus of elasticity, and permeability. The relationship between strength and fiber content was analyzed to understand the impact of coir fiber on the properties of coir fiber-reinforced cement-based concrete. Based on the results obtained, it is determined that 2% coir fiber modification offers the highest compressive strength, splitting tensile strength, and flexural strength. Moreover, the modulus of elasticity is increased, and the permeability is plummeted by the volume fractions of coir fiber 1%, 2%, and 3% because the blending of coir fiber has a bridging and dispersing mechanism of the force-carrying capacity in concrete. In conclusion, coir fiber might be a viable choice for improving the strength and durability of concrete. Therefore, the sparing use of coir fiber presented in this research can be implemented for the manufacturing of concrete in the future.

The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed.

The extensive use of Portland cement (PC) in the manufacturing of concrete is responsible for the depletion of natural resources that are part of cement production. Cement supply is permanently threatened by the ongoing depletion of natural materials, including sand, limestone, and clay. Concurrently, the incineration of agricultural residues presents a significant ecological problem. This study explores the substitution of cement in concrete with 5%, 10%, 15%, and 20% wheat straw ash as an environmentally friendly alternative. The purpose of this investigation is to evaluate the effect of substituting wheat straw ash (WSA) for PC on the mechanical characteristics of concrete. A total of 75 concrete samples were made by cement or cement + WSA/fine aggregate/coarse aggregate ratio of 1:1, 5:3, and water-to-cement ratio was kept constant at 0.50. All of these specimens were cured and tested at 28 days. The properties tested in the paper were workability, compressive strength, splitting tensile strength, flexural strength, modulus of elasticity, and permeability. The outcomes showed that the substitution of PC with WSA 10% resulted in the greatest concrete strength. In contrast, the mechanical properties and permeability of concrete were reduced when 20% WSA was substituted for PC at 28 days. In addition, the slump value dropped as increasing the content of WSA diminished the weight of PC in the concrete. This could be attributed to the fact that the water content in the WSA 20% concrete was not enough for mechanical strength. Other concretes with WSA showed similar properties to those of the WSA 10% concrete. It was concluded from the results that since the WSA 10% concrete showed the best properties, it can be recommended as the best recipe in this research work.

The transformation of conventional binder and grout into high-performance nanocarbon binder and grout was evaluated in this investigation. The high-performance nanocarbon grout consisted of grey cement, white cement, lime, gypsum, sand, water, and graphite nanoplatelet (GNP), while conventional mortar is prepared with water, binder, and fine aggregate. The investigated properties included unconfined compressive strength (UCS), bending strength, ultrasound pulse analysis (UPA), and Schmidt surface hardness. The results indicated that the inclusion of nanocarbon led to an increase in the initial and long-term strengths by 14% and 23%, respectively. The same trend was observed in the nanocarbon binder mortars with white cement, lime, and gypsum in terms of the UCS, bending strength, UPA, and Schmidt surface hardness. The incorporation of nanocarbon into ordinary cement produced a high-performance nanocarbon binder mortar, which increased the strength to 42.5 N, in comparison to the 32.5 N of the ordinary cement, at 28 days.

The simulation elevation-surface area-storage interrelationship of a reservoir is a crucial task in developing ideal water release policies for reservoir and dam operations. In this study, an inclusive (stochastic dynamic programming-artificial neural network (SDP-ANN)) model was established and applied to obtain an ideal reservoir operation strategy for Sg. Langat reservoir in Malaysia. The problems associated with the management of water resources mostly relate to uncertainty and the stochastic nature of the reservoir inflow, and the SDP-ANN model is meant to consider uncertainty in the input parameters such as reservoir inflow and reservoir evaporation losses. The performance of the SDP-ANN model was compared to that of the stochastic dynamic programming-autoregression (AR) model. The primary aim of the model is to decrease the squared deviation from the desired water release, which we determined by comparing the SDP-AR and SDP-ANN model performances. The results indicate that the SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit. Consequently, the case study results confirm that the SDP-ANN model performs better than the SDP-AR model in obtaining the best parameters for the reservoir operation. Specifically, a comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively.

Extensive green roof is one of the sustainable urban stormwater management alternatives to manage and mitigate the urban surface runoff. In order to implement green roofs more effectively, suitable plant species and substrate components for tropical climate must be identified. The aim of this study is to investigate the evapotranspiration (ET) behaviors in extensive green roofs based on different substrate types and local native plant species. Four green roof test beds containing pro-mixing pot and burn soils were each vegetated with Axonopus Compressus (grass) and Portulaca Grandiflora (sedum). A weather station with soil moisture sensors was installed to measure the weather and soil moisture data. The results showed that the mean ET rates for grass-pot soil, sedum-pot soil, grass-burn soil and sedum-burn soil were 1.32 ± 0.41 mm/day, 2.31 ± 0.72 mm/day, 1.47 ± 0.39 mm/day and 2.31 ± 0.43 mm/day, respectively. It is noted that environmental parameters such as ambient temperature, solar radiation and wind speed showed significantly positive relationship (p value < 0.01) with ET rates of green roofs except relative humidity. The crop coefficients (K_s) for the studied green roof plant species are estimated based on actual and reference evapotranspiration rates. The sedum planted in burn soil showed the highest crop coefficient (0.64), followed by sedum in pot soil (0.62), grass in burn soil (0.39) and grass in pot soils (0.37), respectively. The findings in this study also showed that substrate with better water retention capacity generally improved the K_s values.