The splitting tensile strength of concrete is crucial for structural integrity, as tensile stresses from load and environmental changes often lead to cracking. This study investigates the effectiveness of advanced ensemble machine-learning models, including LightGBM, GBRT, XGBoost, and AdaBoost, in accurately predicting the splitting tensile strength of silica fume-enhanced concrete. Using a robust database split into training (80%) and testing (20%) sets, we assessed model performance through R², RMSE, and MAE metrics. Results demonstrate that GBRT and XGBoost achieved superior predictive accuracy, with R² scores reaching 0.999 in training and high precision in testing (XGBoost: R² = 0.965, RMSE = 0.337; GBRT: R² = 0.955, RMSE = 0.381), surpassing both LightGBM and AdaBoost. This study highlights GBRT and XGBoost as reliable, efficient alternatives to traditional testing methods, offering substantial time and cost savings. Additionally, SHapley Additive exPlanations (SHAP) analysis was conducted to identify key input features and to elucidate their influence on splitting tensile strength, providing valuable insights into the predictive behavior of silica fume-enhanced concrete. The SHAP analysis reveals that the water-to-binder ratio and curing duration are the most critical factors influencing the splitting tensile strength of silica fume concrete.

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations.

The cloud paradigm has swiftly developed, and it is now well known as one of the emerging technologies that will have a significant influence on technology and society in the next few years. Cloud computing also has several benefits, including lower operating costs, server consolidation, flexible system setup, and elastic resource supply. However, there are still technological hurdles to overcome, particularly with real-time applications by providing resources. Resources allocation management most charming part of cloud computing; therefore, several authors have worked in the area of resource usage. This study introduces an innovative cloud machine learning framework-based linear regression approach called cloud linear regression (CLR), which entails both cloud technology and machine learning concept. CLR using machine learning yielded good prediction results for resource allocation management, as appeared with many researching, and still seek, research to raise optimal solutions to the resources' allocation problem as the aim of this study. This study discusses the relation between cloud resource allocation management and machine learning techniques by illustrating the role of linear regression methods, resource distribution, and task scheduling. The analytical analysis shows that the CLR promises to present an effective solution for resources (scheduling, provisioning, allocation, and availability).

Cloud computing is an evolving and high-demand research field at the forefront of technological advancements. It aims to provide software resources and operates based on service-oriented delivery. Within the infrastructure as a service (IaaS) framework, the cloud offers end customers access to crucial infrastructure resources, including CPU, bandwidth, and memory. When a cloud system fails to deliver as expected, it is referred to as an event, signifying a deviation from the anticipated service. To meet their service-level agreement (SLA) obligations, cloud service providers (CSPs) must ensure continuous access to fault-tolerant, on-demand resources for their clients, particularly during outages. Consequently, finding the most efficient ways to accomplish tasks while considering the rapid depletion of resources has become an urgent concern. Researchers are actively working to develop optimal strategies tailored to the cloud environment. Machine learning plays a critical role in these endeavors, serving as a key component in various cloud computing platforms. This study presents a comprehensive literature review of current research papers that employ machine learning algorithms to propose strategies for optimizing cloud computing environments. Additionally, the survey provides authors with invaluable resources by extensively exploring a diverse range of machine learning techniques and their applications in the field of cloud computing. By examining these areas, researchers aim to enhance their understanding of efficient resource allocation and scheduling, addressing the challenges posed by resource scarcity while meeting SLA obligations. Index Terms— Cloud computing, Resource allocation Prediction, Energy-efficient resource management, Task Scheduling, Machine Learning.

The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, capacity amendments, or interface bug fixes are performed. These modifications are based on the level of semantic obscurations observed during the application service intervals. The obscuration level is determined using knowledge learning as a progression from hardware to software semantics. The results reported were computed using specific metrics obtained from these experimental evaluations: an 8.94% reduction in interfacing complexity and a 15.04% improvement in integration progression. The knowledge of obscurations maps the modifications appropriately to reinstate the agility testing of the hardware/software integrations. This modification-based semantics is verified using semantics error, modification time, and complexity.

Hydrochar is a carbon-rich material produced through the hydrothermal carbonization (HTC) of wet biomass such as sewage sludge. Its nitrogen content is a critical quality parameter, influencing its suitability for use as a soil amendment and its potential environmental impacts. This study develops a high-accuracy ensemble machine learning framework to predict the nitrogen content of hydrochar derived from sewage sludge based on feedstock compositions and HTC process conditions. Four ensemble algorithms—Gradient Boosting Regression Trees (GBRTs), AdaBoost, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)—were trained using an 80/20 train–test split and evaluated through standard statistical metrics. GBRT and XGBoost provided the best performance, achieving R² values of 0.993 and 0.989 and RMSE values of 0.169 and 0.213 during training, while maintaining strong predictive capabilities on the test dataset. SHAP analyses identified nitrogen content, ash content, and heating temperature as the most influential predictors of hydrochar nitrogen levels. Predicting nitrogen behaviour during HTC is environmentally relevant, as the improper management of nitrogen-rich hydrochar residues can contribute to nitrogen leaching, eutrophication, and disruption of aquatic biogeochemical cycles. The proposed ensemble-based modelling approach therefore offers a reliable tool for optimizing HTC operations, supporting sustainable sludge valorisation, and reducing environmental risks associated with nitrogen emissions.