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The high-density urban form and building arrangement of modern cities have contributed to numerous environmental problems. The calm wind area caused by inappropriate building arrangements results in pollutant accumulation. To realize a practical design and improve urban microclimate, we investigated the spatial relationship between roads, buildings, and open space using the machine learning technique. First, region growing and k-means clustering were employed to identify roads and buildings. Based on the image masking program, we selected training areas according to the land use map. Second, we used the multiple-point statistics technique to create new urban fabric images. Viewing the training image as a prior model, our program constantly reproduced morphological structures in the target area. We intensified the similarity with training areas and enriched the variability among generated images. Third, Hausdorff distance and multidimensional scaling were applied to achieve a quality examination. The proposed method was performed to fulfill an urban renovation design in Xi'an, China. Based on the historical record, we applied computational fluid dynamics to simulate air circulation and ventilation. The results indicate that the size of calm wind area is reduced. The wind environment is significantly improved due to the rising wind speed. [ABSTRACT FROM AUTHOR]

The process of geopolymerization affects the freshness and hardening properties of fly ash base polymer. The prediction of geological polymerization parameters, such as DPT, DPH, GPT, and GPH, is very important for the mixing optimization of FA base polymer. In this study, machine learning models such as backpropagation neural network, support vector regression, random forest, K-nearest neighbor, logistic regression, and multiple linear regression were used to predict the above geological polymerization parameters and explain the influence of composition on the geological polymerization of FA base polymer. Results show that RF was the most stable ML model and had the best predictive performance on the test sets of GPT, GPH, DPT, and DPH, with correlation coefficients of 0.88, 0.95, 0.92, and 0.95, respectively. The variable importance and sensitivity were analyzed by SHapley Additive exPlanations. Results indicate that temperature is the most significant input variable affecting the DPT, DPH, and GPH with SHAP values of 0.09, 4.83, and 1.03, respectively. For GPT, the SHAP value of temperature is 6.89, slightly lower than that of LFR (6.95); yet it is a still significantly important input variable. The mole ratio and alkaline solution concentration were also important and negatively contributed to DPT and DPH, respectively. Besides, both GPT and GPH were sensitive to the mass ratio of liquid-to-fly ash which can promote the geopolymerization extent and shorten the geopolymerization time at a small content. The results of this study pave the way for automatic mixture optimization of FA-based geopolymers. [ABSTRACT FROM AUTHOR]

Summary: Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k‐nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five‐fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Lightweight concrete (LWC) is widely used in the construction industry due to a variety of advantages. However, compared with traditional normal-weight concrete, more influencing variables (e.g. types of lightweight aggregates) must be considered to optimize multiple properties including uniaxial compressive strength (UCS), density and cost. This makes the mixture design of LWC more difficult or sometimes impossible using laboratory experiments. To address this issue, this study proposes a multi-objective optimization (MOO) method using machine learning and metaheuristic approaches for LWC mixture design through a two-step approach. In the first step, a least squares support vector regression (LSSVR) model is constructed to predict multiple properties of LWC. The hyper-parameters of the LSSVR model are tuned using the firefly algorithm (FA). A dataset containing a large number of different mixtures of LWC is compiled from published literature. High prediction accuracy (0.97 for UCS and 0.90 for density) is achieved on the test dataset (including 30% of all the instances). In the second step, a newly developed multi-objective FA (MOFA) model is used to optimize the LWC mixture, while satisfying the constraints. The Pareto fronts of the triple objectives (UCS, cost and density) are successfully obtained. The proposed MOO method is powerful and efficient in finding optimal LWC mixtures with conflicting objectives and therefore decision making can be facilitated in early phases of construction. [ABSTRACT FROM AUTHOR]

• The UCS of SFC are predicted using BPNN models. • MOBAS is developed for multi-objective mixture optimization of SFC. • A GUI is developed for mixture optimization of SFC. Partial replacement of cement by silica fume in concrete provides advantages such as mitigation of the impact on the environment of carbon dioxide emitted during cement production, recycling of industrial by-products and improvement of concrete strength and durability. The optimization of the mixture of silica fume concrete (SFC) requires trade-off among multiple objectives (strength, cost and embodied CO 2) and consideration of a large number of variables under highly nonlinear constraints. Obtaining the Pareto front of this multi-objective optimization (MOO) problem is computationally expensive. To address this issue, the present study develops a MOO model using machine learning (ML) techniques and a new meta-heuristic algorithm. Firstly, the relationships between components and SFC properties are modelled on a dataset using a back propagation neural network (BPNN) model. Then an individual-intelligence-based multi-objective beetle antennae search algorithm (MOBAS) is developed to search for optimal SFC mixtures that maximize UCS, and minimize cost and embodied CO 2 under defined constraints. Results indicate that the proposed MOBAS is more computationally efficient with satisfactory accuracy in comparison with algorithms based on swarm intelligence. The MOO model achieves reliable predictions for UCS with a very high correlation coefficient (0.9663) on the test set. The Pareto front of optimal SFC mixture proportions of the MOO problem is successfully obtained using the proposed model. The proposed frame-work improves the efficiency in SFC mixture optimization and can facilitate appropriate decision making before construction. [ABSTRACT FROM AUTHOR]

• BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump. • PSO is efficient in tuning hyperparameters of machine learning models. • The Pareto front of the mixture optimization problem is obtained by MOPSO. For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase. [ABSTRACT FROM AUTHOR]

Depletion of river sand due to large-scale concrete production has caused many environmental problems. To address this issue, river sand can be replaced with sand manufactured from waste deposits. To facilitate manufactured-sand concrete production, this study proposes three tree-based models: one individual model (regression tree (RT)), and two ensemble models (random forest (RF) and gradient boosted regression tree (GBRT)) to predict its mechanical properties, such as uniaxial compressive strength (UCS), and splitting tensile strength (STS). These tree-based models were trained and tested on a dataset collected from previous literature. In addition, to understand the importance of each input variable on the mechanical properties of manufactured-sand concrete, the variable importance is calculated using the RF algorithm. The results show that the highest correlation coefficients are achieved by GBRT in predicting UCS (0.9887) and STS (0.9666), which respectively increase by 3.0%–10.8% and 16.0%–21.6% in comparison with the models in previous literature. The mechanical properties UCS and STS are highly sensitive to the curing age with relative importance of 36.8% and 40.3%, respectively. To facilitate the application of the tree-based models in predicting mechanical properties of manufactured-sand concrete, a graphical user interface has been designed in this study. • The mechanical properties of manufactured-sand concrete were predicted by tree-based models. • Firefly algorithm was used to tune the hyperparameters of the tree-based models. • Importance of input variables was calculated using random forest algorithm. • A GUI was developed to facilitate the application of the proposed models. [ABSTRACT FROM AUTHOR]

• A GA-RF model was developed for predicting UCS and FST of AAMs; • The curing time and water content significantly influenced the UCS, while Na/Al and water content were more important to FST; • The recommended Ca/Si varied from 1 to 2; Na/Al was slightly lower than 1 and Si/Al ratios changed between 2.5 and 3.5. The final setting time (FST) and uniaxial compressive strength (UCS) are critical parameters for designing the mixture proportions of alkali-activated materials (AAMs). To understand the influence of the mixture composition on FST and UCS of AAMs, two datasets containing 616 samples for UCS and 278 samples for FST were compiled from published literature. A random forest (RF) model was developed on these datasets to predict FST and UCS of AAMs. The hyperparameters of the RF model were optimized using the Genetic Algorithm (GA). Results show that the hybrid GA-RF model achieved the highest prediction accuracy on the test set of UCS (0.932) and FST (0.997), compared to other machine learning models. The developed model was then used to interpret the influence of mixture composition on FST and UCS. The curing time and water content significantly influenced the UCS, while Na/Al and water contents were more important to FST. The microstructure development of the AAMs was affected by Ca/Si, Na/Al and Si/Al ratios. To achieve better UCS, the recommended Ca/Si varied from 1 to 2; Na/Al was slightly lower than 1 and Si/Al ratios changed between 2.5 and 3.5. This study can facilitate the mixture optimization for FA-slag based AAMs. [ABSTRACT FROM AUTHOR]

The precast segmental self-centering concrete-filled steel tube (PSCFST) bridge is not only the ideal choice for fast and environmentally friendly construction but also has good seismic and resilience properties. Our research group has carried out shaking table test research on the PSCFST bridge, but due to the limitation of test equipment and site, no damage test has been carried out. To further study the seismic performance of PSCFST bridges when subjected to larger ground motions, machine learning (ML) models are developed to predict the seismic performance of PSCFST. A novel combined prediction model based on Conv1D-LSTM was proposed to predict the PSCFST bridge acceleration response. Two other commonly used ML methods including XGBoost and Random forest regression (RFR) are also used for comparison purposes. A database of ML prediction models is established based on 116 sets of input ground motion (GM) and superstructure acceleration response from shaking table tests. Then, the data of RSN292 60% GM were selected as the prediction test data. Furthermore, based on the Opensees platform, the PSCFST fiber finite element (FFE) model was established and validated by the shaking table test results, then the dynamic time history analysis of the ground motion with larger amplitude (greater than the input ground motion assignment of the shaking table test) was carried out. The superstructure acceleration response of 70%–120% of the RSN 292 GM is obtained by the FFE model and used as the data set for the ML prediction model. After that, the superstructure acceleration response is obtained through three prediction models. Comparing the simulation and prediction results shows that all the Conv1D-LSTM, XGBoost, and RFR models can reliably predict the acceleration response of the PSCFST bridge. In all cases, the Conv1D-LSTM model performed outperforms the XGBoost and RFR models. The determination coefficients (R 2) of Conv1D-LSTM, XGBoost, and RFR model for the prediction of superstructure response are 0.9643, 0.8780, and 0.9623, respectively. • Proposed a novel combined prediction model based on Conv1D-LSTM to predict the PSCFST bridge acceleration response. • Established the fiber finite element analysis model of PSCFST single span bridge and validated by the shaking table test. • Three AM models are used to predict the acceleration response of the PSCFST bridge under large amplitude ground motions. [ABSTRACT FROM AUTHOR]