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This research presents a new model for finding optimal conditions in the concrete technology area. To do that, results of a series of laboratory investigations on concrete samples were considered and used to design several artificial intelligence (AI) models. The data samples include 8 parameters i.e., silica fume replacement ratio, fly ash replacement ratio, fine aggregate, water content, high rate water reducing agent, coarse aggregate, total cementitious material, and age of samples, were used to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO) algorithm to find the optimal results as well as optimizing the kernel coefficients of the support vector regression (SVR) models. Initially, to form the core of this research, various models were constructed and proposed to design the required relationship between the data using SVR. Since different SVR kernels have their own coefficients, using optimization theory, the probability of error in the models was reduced and the models were identified and executed with the highest accuracy. Finally, the polynomial model was selected as the model with the lowest computational error and the highest accuracy for evaluating the compressive strength of the concrete samples. The accuracy of the proposed SVR model for training and testing data was obtained as the coefficient of determination (R2) = 0.9694 and R2 = 0.9470, respectively. This function was considered as a relation, to be developed by the HLO algorithm to find optimal options under different conditions. The results for 14 samples, which are the most important examples of this research, showed that the optimal states are obtained with a high level of accuracy. This confirms the proper use/develop of the SVR-HLO algorithm in designing the predictive model as well as finding optimal conditions in the concrete technology area.

Shear design of RC beams with and without stirrups using laboratory experiments is difficult or even impossible as a large number of variables need to be considered simultaneously, such as the span-to-depth ratio, web width and reinforcement ratio. In addition, due to the complex shear failure mechanism, empirical approaches for shear design are derived within the boundaries of their own testing regimes. Thus, the generalization ability and applicability of these approaches are limited. To address this issue, this study uses machine learning approaches for shear design. A random forest model is constructed to predict the shear strength of RC beams. The hyperparameters of RF are tuned using beetle antennae search algorithm modified by Levy flight and inertia weight. The developed model is trained on two data sets of RC beams with and without stirrups containing 194 and 1849 samples, respectively. The obtained model has high prediction accuracy with correlation coefficients of 0.9367 and 0.9424 on these two test data sets, respectively. The proposed method is powerful and efficient in shear design of RC beams with and without stirrups and therefore paves the way to intelligent construction.

Using hydraulic fracturing for cutting roof pressure is a critical technology to protect coal pillars. In this paper, based on the engineering background of 18506 working face in the Xiqu Coal Mine, using the methods of theoretical analysis, numerical simulation, and field measurement, a reasonable coal pillar width and practical parameters of hydraulic fracturing are given. The results show that roof cutting can significantly increase the stress in goaf and relieve the advanced pressure of the working face. Taking 18506 working face as the research object, the industrial test is carried out, and the surrounding rock control scheme of hydraulic fracturing and roof cutting is put forward, the mine pressure monitoring results show that the auxiliary roadway of 18506 working face reaches a stable state within 20 days, the deformation and damage degree of roadway surrounding rock are small, and the integrity of surrounding rock is improved.

The roadway instability in deep underground conditions has attracted constant concerns in recent years, as it seriously affects the efficiency of coal mining and the safety of personnel. The large rheological deformations normally occur in deep roadway with soft coal mass. However, the failure mechanism of such roadways is still not clear. In this study, based on a typical soft coal roadway in the field, the in-situ measurements and rock mass properties were obtained. The rheological deformation of that roadway was revealed. Then a time-dependent 3D numerical model was established and verified. Based on the verified model, the deformation properties and evolutionary failure mechanism of deep coal roadway were investigated in detail. The results showed that the deformation of the soft coal roadway demonstrated rheological behavior and the applied support structures failed completely. After roadway excavation, the maximum and minimum stresses around the roadway deteriorated gradually with the increase of time. The failure zones in soft coal mass expanded increasingly over time, which had a negative effect on roadway stability in the long-term. According to the findings, helpful suggestions were further presented to control the rheological deformation in the roadway. This research systematically reveals the instability mechanism of the deep coal roadway and provides some strategies for maintaining roadway stability, which can significantly promote the sustainability of mining in deep underground coal mines.

In maintaining the efficiency of coal mining, the stability of roadway plays a significant role, as it is closely related to the production of coal and the safety of personnel. In deep underground coal mines, the rheological deformation of roadway normally occurs, which affects its service life. To address this problem, in this paper, a novel high-efficiency Jet Grouting (JG) technique was presented, and its control effect on roadway stability was investigated. A creep test of a coal specimen in a laboratory scale was performed, and its creep behavior was revealed. The rheology of the coal mass surrounding the roadway was further analyzed according to the field-monitoring results of roadway deformation. A time-dependent numerical model with a Burger-creep visco-plastic model (CVISC) was established and validated by comparing the calculated displacement with in-situ measurement. The JG technique was tested in the field, and its applicability and practicability were confirmed. According to the validated model and field test results of JG, a numerical model with CVISC by JG support was established to analyze the effect of JG on the roadway. The results showed that the JG support can effectively reduce roadway deformation, optimize stress conditions, and reduce the extent of the plastic zone around the roadway. The rheological properties of the soft coal roadway were constrained and long-term stability was ensured. This pioneering work can guide the application of JG for the stability control of roadways and promote the sustainability of coal mining efficiently.

Rubberized concrete (RC) has attracted more attention these years as it is an economical and environmental-friendly construction material. Normally, the uniaxial compressive strength (UCS) of RC needs to be evaluated before application. In this study, an evolutionary random forest model (BRF) combining random forest (RF) and beetle antennae search (BAS) algorithms was proposed, which can be used for establishing the relationship between UCS of RC and its key variables. A total number of 138 cases were collected from the literature to develop and validate the BRF model. The results showed that the BAS can tune the RF effectively, and therefore, the hyperparameters of RF were obtained. The proposed BRF model can accurately predict the UCS of RC with a high correlation coefficient (0.96). Furthermore, the variable importance was determined, and the results showed that the age of RC is the most significant variable, followed by water-cement ratio, fine rubber aggregate, coarse rubber aggregate, and coarse aggregate. This study provides a new method to access the strength of RC and can efficiently guide the design of RC in practice.

The present study aims to examine the effect of modifiers (Styrene-Butadiene-Styrene and crumb rubber) on the rutting, moisture-induced damage, and workability properties of hot mix asphalt (HMA) mixtures. In this study, three types—namely, control (CB), crumb rubber-modified (CRMB), and polymer-modified (PMB)—of mixtures/binders were evaluated. The rutting properties were evaluated using a wheel tracking device and the Multiple Stress Creep Recovery (MSCR) test. The moisture-induced damage properties were evaluated using the Indirect Tensile Strength (modified Lottman) and bitumen bond strength (BBS) tests. The workability properties were evaluated using densification indices (Bahia and locking point method) and a viscosity test. The results indicate that CRMB mixtures were less workable and exhibited a better resistance to rutting than the PMB and CB mixtures. Further, the PMB mixtures had increased resistance to moisture-induced damage, while the effect of the CRMB mixtures was negligible compared to the CB mixtures.

Pre-grouting as an effective means for improving the stability of roadways can reduce maintenance costs and maintain safety in complex mining conditions. In the Guobei coal mine in China, a cement pre-grouting technique was adopted to enhance the overall strength of soft coal mass and provide sufficient support for the roadway. However, there are very limited studies about the effect of grouting on the overall strength of coal in the laboratory. In this paper, based on the field observation of a coal-grout structure after grouting, a series of direct shear tests were conducted on coal and grouted coal specimens to quantitatively evaluate the quality improvement of grouted coal mass. The results showed that the peak and residual shear strength, cohesion, friction angle and the shear stiffness of grouted coal were significantly improved with the increase of the diameter of grout column. Linear regression models were established for predicting these mechanical parameters. In addition, three failure models associated with coal and grouted coal specimens were revealed. According to microstructure and macroscopic failure performance of specimens, the application of the proposed models and some methods for further improving the stability of grouted coal mass were suggested. The research can provide the basic evaluation and guideline for the parametric design of cement pre-grouting applications in soft coal mass.

Coal-grout composites were fabricated in this study using the jet grouting (JG) technique to enhance coal mass in underground conditions. To evaluate the mechanical properties of the created coal-grout composite, its unconfined compressive strength (UCS) needed to be tested. A mathematical model is required to elucidate the unknown nonlinear relationship between the UCS and the influencing variables. In this study, six computational intelligence techniques using machine learning (ML) algorithms were used to develop the mathematical models, which includes back-propagation neural network (BPNN), random forest (RF), decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR). In addition, the hyper-parameters in these typical algorithms (e.g., the hidden layers in BPNN, the gamma in SVM, and the number of neighbor samples in KNN) were tuned by the recently developed beetle antennae search algorithm (BAS). To prepare the dataset for these ML models, three types of cementitious grout and three types of chemical grout were mixed with coal powders extracted from the Guobei coalmine, Anhui Province, China to create coal-grout composites. In total, 405 coal-grout specimens in total were extracted and tested. Several variables such as grout types, coal-grout ratio, and curing time were chosen as input parameters, while UCS was the output of these models. The results show that coal-chemical grout composites had higher strength in the short-term, while the coal-cementitious grout composites could achieve stable and high strength in the long term. BPNN, DT, and SVM outperform the others in terms of predicting the UCS of the coal-grout composites. The outstanding performance of the optimum ML algorithms for strength prediction facilitates JG parameter design in practice and could be the benchmark for the wider application of ML methods in JG engineering for coal improvement.

The efficiency of coal mining is seriously affected by roadway stability, as large time‐dependent deformation of roadway frequently occurs and needs to be maintained several times during its service life. Such rheological deformation was common in soft coal mass at Huaibei coalfield in China. To address this issue, in this study, the time‐dependent deformation of the soft coal roadway was analyzed and a new Jet Grouting (JG) technique was presented for controlling deformation. The time‐dependent deformation of the soft coal roadway was numerically simulated and validated. Based on the field test results and the verified model, a JG support model was established to examine its effect on roadway deformation. The JG support system can reduce the horizontal and vertical displacement of the roadway effectively and constrain the time‐dependent deformation of coal mass. The deformation rate and stabilization time of roadway decreased significantly by comparison with conventional support. This work presented a promising JG support scheme for controlling the time‐dependent deformation in the roadway in deep underground mine, which can greatly promote the JG design and application. We first revealed the time‐dependent behavior of roadway in the deep underground coal mine. A high‐efficiency jet grouting technique on controlling rheological deformation was first evaluated by a time‐dependent 3D numerical model. The results are encouraging and the pioneering work can improve the safety of the coal roadway and the efficiency of coal mining.