Mohammed Amin Benbouras

Ensuring bitumen quality is essential for the durability of asphalt pavements. Conventional laboratory tests for softening point (SP) and penetration (P) are reliable but labor-intensive and time-consuming, particularly for modified binders incorporating recycled additives. This study proposes an advanced hybrid machine learning (ML) framework integrating Neural Architecture Search (NAS) with Artificial Neural Networks (ANN) to predict the SP and P of modified bitumen containing crumb rubber (CR), coffee grounds (CG), and olive pomace (OP). A dataset of 49 laboratory-prepared samples was used to train and validate three models – Random Forest (RF), ANN, and NAS-ANN – evaluated through statistical indicators (MSE, RMSE, R2, R). The NAS-ANN achieved the best performance, with R = 0.9665/0.9567 for SP and 0.9726/0.8711 for P (training/validation). Sensitivity analysis indicated that OP had the most significant influence on SP, followed by CR and CG. The optimized NAS-ANN model was implemented in a user-friendly interface named SmartBit-AI, designed to facilitate rapid prediction and mix-design preselection. Overall, the proposed NAS-ANN framework complements conventional laboratory testing, offering a practical and efficient tool for accelerating sustainable bitumen evaluation and design.

Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world.

Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a K-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.

Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modeling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are utilized to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modeling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies.

Climatic spatial maps are essential for understanding the thermal conditions of cities and estimate their cooling and heating energy needs. Climate maps allow building designers and city planners to get adequately informed without accessing, analyzing or interpreting dense textual information. In this study, a representative residential benchmark model was simulated in seventy-four cities of Algeria. The simulation results were interpolated using geographic information systems to generate six high-resolution maps that spatially estimate and visualize the discomfort hours and cooling/heating energy needs. The unique methodology relies on a reliable weather dataset (2004–2018) and combines the power of building performance simulation and geographic information systems. The results of these analyses provide easy to understand and web-based atlas that can be used to explore regional and local climate and quantify the discomfort hours, the heating/cooling energy needs and energy use intensity. The spatial maps are not a static product, but rather data-rich content, which can be expanded to include the most important cities of Algeria. The capabilities of the tool allow architects and urban planners to understand the climate better and propose practical design guidance.