The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

Over the last decades, cement has been observed to be the most adaptive material for global development in the construction industry. The use of ordinary concrete primarily requires the addition of cement. According to the record, there has been an increase in the direct carbon footprint during cement production. The International Energy Agency, IEA, is working toward net zero emissions by 2050. To achieve this target, there should be a decline in the clinker-to-cement ratio. Also, the deployment of innovative technologies is required in the production of cement. The use of alternative binding materials can be an easy solution. There are several options for a substitute to cement as a binding agent, which are available commercially. Non-crystalline alkali-aluminosilicate geopolymers have gained the attention of researchers over time. Geopolymer concrete uses byproduct waste to reduce direct carbon dioxide emissions during production. Despite being this advantageous, its utilization is still limited as it shows the quasi-brittle behavior. Using different fibers has been started to overcome this weakness. This article emphasizes and reviews various mechanical properties of fiber-reinforced geopolymer concrete, focusing on its development and implementation in a wide range of applications. This study concludes that the use of fiber-reinforced geopolymer concrete should be commercialized after the establishment of proper standards for manufacturing.

Sustainability and the need for biodegradable composites are the two new buzzwords finding prominence in recent times. In this regard, there has been a significant increase in interest regarding natural fiber composites (NFCs) due to their potential to address environmental issues and contribution toward the development of sustainable materials. The aim of this extensive review is to thoroughly investigate the current research landscape, advancements, and applications of NFCs. The study covers a range of topics, including the various types of natural fibers commonly used, their processing techniques, reinforcement strategies, mechanical properties, morphological characteristics, biodegradability, and sustainability. It also explores their potential uses across different industries. Furthermore, this article examines the challenges and opportunities associated with NFCs, as well as their environmental impacts and economic viability. By conducting a detailed analysis of existing literature, this review aims to shed light on the advancements achieved so far and the future potential of NFCs as eco-friendly alternatives to conventional materials. Ultimately, it offers insights into how these materials can enhance sustainability in material engineering.

In the present study, the quality of the surface water of the Gomti river (Lucknow, India) was investigated. Lucknow is situated in the centre of Uttar Pradesh, which is most the populated state in India. The locality has experienced rapid, unregulated development activities and population growth in recent decades, both of which have had a negative impact on its ecosystem and environment. Continuous monitoring is required to maintain the ecosystem at the desired level. Nine samples of river water were collected from the Gomti River in Lucknow, and they were analysed for a total of nine different characteristics, including pH, turbidity (Tur), dissolved oxygen (DO), total dissolved solids (TDSs), chemical oxygen demand (COD), chloride ion (Cl-) concentration, temperature (T), biochemical oxygen demand (BOD₅) and total hardness (TH). The observed data were analysed using multivariate statistical methods. A cluster analysis (CA) was used to sort the sampling locations into different groups, and a principal component analysis (PCA) was used to find the different sources of pollution. Using a cluster analysis, all the water quality parameters were divided into three groups. Cluster 1 represented the less polluted sites, cluster 2 represented the moderately polluted sites and cluster 3 represented the highly polluted sites. Sampling sites SS8, SS4, S99 and SS7 were highly polluted because of nearby pollution sources such as domestic wastewater and runoff storm water. The principal component analysis yielded two meaningful components that explained 82.4% of the total variation in the data. The first factor and second factor explained 59.022 and 23.363 percentages of the total variance, respectively. It was noticed that major sources of pollution for the Gomti river are storm water runoff and the release of domestic and industrial wastewater from residents and industries, respectively. This study will help policy makers to ensure sustainable practices and reduce negative impacts on the availability and quality of water, allowing for the most efficient use of the Gomti River.

The study of embankment breaching is not an easy practice, as it includes various parameters to meet the suitability of the design approach, especially when we consider it for the long term. Embankment breach studies generally include the prediction of different breach parameters. The important physical and hydrodynamic parameters of the flood wave generated from the embankment failure are breach width, breach slope, formation time, peak outflow, and time to failure. Out of these parameters, peak outflow is a very important breach parameter, as it deflects the magnitude of destruction on the downstream side of the embankment and affects the evacuation plans for the downstream population. The prediction of breach peak outflow due to overtopping of the embankment is very essential for dam failure prevention and mitigation, as well as for the design of an early warning system. Many researchers have used dam failure data, comparative studies, experimental studies, or regression techniques to develop various models for predicting peak outflow. The present paper analyzes the results of the design for forty experiments carried out in two different laboratory water channels (flumes). Different embankment models are overtopped with the objective of studying the breach behavior during overtopping. The study was inspired by reports in the open literature of embankment failures that resulted in catastrophic conditions. With experimental data, an efficient model is developed for predicting breach peak outflow (Q_p) by correlating with other independent embankment breach parameters for cohesive as well as non-cohesive embankments. The model is validated with historical and laboratory data compiled in the past. For the validation of current investigational work, the experimental data of the present study are compared with the model already developed by other researchers. From the study and analysis, it is observed that breach peak outflow depends upon hydraulic, geometric, and geotechnical parameters of embankments. Being a phenomenon that is active for a short duration only, the manual measurement of various parameters of the modeling process poses some limitations under laboratory conditions.

The diminution of natural resource exploration, the retrieval of waste, and the structural modification of polymers by additives are the main contributions to sustainable development. The properties of bitumen are enhanced by the crumb rubber through effective bitumen modification techniques, which have environmental and economic advantages. In this study, plastic waste, plastic-coated aggregate (PCA), and bitumen were blended in order to enhance the engineering properties of the flexible pavement. In order to compute the composition of crumb rubber modified bitumen (CRMB), the adopted materials were subjected to the relevant experiments. PCA was a very effective material when compared to the standard bitumen road pavement. The recycling of waste crumb rubber and plastic was tested by adding them into the hot mix asphalt. The Marshall properties of standard (virgin) bituminous mix, CRMB grade 55, and plastic mix asphalt were studied in detail to explore the solutions for a sustainable environment. The comparison was performed between these two materials with the standard bitumen, which resulted in the CRMB and plastics being found as the most effective additions with robust properties such as low-cost material, high strength, long life usage, and un-harmful to nature. The optimal bitumen content was found to be 6.166%, 6.1%, and 5.833% for standard bitumen, crumb rubber modified bitumen, and plastic-coated aggregate, respectively.