The quest to enhance public health and the need for a reduction in the environmental solid wastes have prompted this study. Despite abundant studies on silica fume (SF or S) and waste glass powder (WGP or G), there is a need to understand the interaction of WGP with SF in the production of ordinary Portland cement (OPC or C)-based concrete using the water/binder ratio of 0.42. The investigated concrete comprised 90 wt.% of OPC and 10 wt.% of WGP+SF. The samples were denoted as C₉₀GxS_10−x such that x varied from 0–10 wt.% at the interval of 2.5. The findings revealed that an increase in the WGP/SF ratio enhanced the absorption of silica/glass blended concrete due to size incompatibility and proliferations of interfacial transition zones between the glass particle, silica fume and cement matrix. The density of fresh OPC concrete was higher than that of glass/silica blended concrete due to the difference in their relative densities. Incorporating WGP and SF in synergy enhanced silicate reorganization and led to a more amorphous binder and a reduction in hydroxyl-based compounds such as portlandite but caused microstructural heterogeneity in the morphology of the binder as obtained from XRD, FTIR and SEM/EDS results. The 28-day compressive strength of 46 MPa is achievable if the WGP and SF are kept within 2.5–5 wt.% and 5–7.5 wt.%, respectively. The study will foster the production of economic, environmental, and cost-efficient concrete.

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.

Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict the compressive strength of high-strength concrete (HSC) using different methods. To achieve this purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), and response surface methodology (RSM) were used as ensemble methods. Using an ANN and ANFIS, high-strength concrete (HSC) output was modeled and optimized as a function of five independent variables. The RSM was designed with three input variables: cement, and fine and coarse aggregate. To facilitate data entry into Design Expert, the RSM model was divided into six groups, with p-values of responses 1 to 6 of 0.027, 0.010, 0.003, 0.023, 0.002, and 0.026. The following metrics were used to evaluate model compressive strength projection: R, R², and MSE for ANN and ANFIS modeling; R², Adj. R², and Pred. R² for RSM modeling. Based on the data, it can be concluded that the ANN model (R = 0.999, R² = 0.998, and MSE = 0.417), RSM model (R = 0.981 and R² = 0.963), and ANFIS model (R = 0.962, R² = 0.926, and MSE = 0.655) have a good chance of accurately predicting the compressive strength of high-strength concrete (HSC). Furthermore, there is a strong correlation between the ANN, RSM, and ANFIS models and the experimental data. Nevertheless, the artificial neural network model demonstrates exceptional accuracy. The sensitivity analysis of the ANN model shows that cement and fine aggregate have the most significant effect on predicting compressive strength (45.29% and 35.87%, respectively), while superplasticizer has the least effect (0.227%). RSME values for cement and fine aggregate in the ANFIS model were 0.313 and 0.453 during the test process and 0.733 and 0.563 during the training process. Thus, it was found that both ANN and RSM models presented better results with higher accuracy and can be used for predicting the compressive strength of construction materials.

The need to get rid of solid waste in the environment necessitates the incorporation of waste glass powder (WGP) in mortar and concrete. The blending of WGP (G) with ordinary Portland cement (OPC) is a valorization technique that is not only cost efficient but also environmentally friendly. The replacement level is denoted as C_xG_10−x, where x is 0–20 wt.% at an interval of 5 wt.% in mortar (w/b = 0.4) and 0, 10, 20 and 30 in concrete (w/b = 0.42). The study investigates the effects of glass on the setting, workability, thermal resistance, microstructure, mineral phases and bond characteristics of silicon and hydroxyl-based compounds and C-O vibrations. It also provides the model equations for strength characteristics in terms of OPC, G and ages in mortar and concrete on one hand and investigates the residual strength and density of glass blended concrete at elevated temperature (550 °C) on the other. It is found that glass enhances the workability, reduces the setting time and density and enhances the residual strength and density of concrete. The presence of glass leads to the formation of coesite and microstructural distortion and decreases the Ca/Si ratio. Besides, the bond characteristics of the binder are significantly affected, while the thermal residual strength capacity in glass blended concrete (C₈₀G₂₀) is 40.4% and 75.14% lower than that in OPC concrete (C₁₀₀G₀) because of the low thermal conduction of glass particles. The optimum glass content in mortar and concrete to produce 33 MPa (28 days) and 47 MPa (90 days) is found to be 10 wt.% and 20 wt.%, respectively.

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m³). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m³. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R², i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering.