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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]

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]

• 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]