A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R²) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that—when compared to the default GBRT model—the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R² values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach.

Unconfined compressive strength (UCS) can be used to assess the applicability of geopolymer binders as ecologically friendly materials for geotechnical projects. Furthermore, soft computing technologies are necessary since experimental research is often challenging, expensive, and time-consuming. This article discusses the feasibility and the performance required to predict UCS using a Random Forest (RF) algorithm. The alkali activator studied was sodium hydroxide solution, and the considered geopolymer source material was ground-granulated blast-furnace slag and fly ash. A database with 283 clayey soil samples stabilized with geopolymer was considered to determine the UCS. The database was split into two sections for the development of the RF model: the training data set (80%) and the testing data set (20%). Several measures, including coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE), were used to assess the effectiveness of the RF model. The statistical findings of this study demonstrated that the RF is a reliable model for predicting the UCS value of geopolymer-stabilized clayey soil. Furthermore, based on the obtained values of RMSE = 0.9815 and R² = 0.9757 for the testing set, respectively, the RF approach showed to provide excellent results for predicting unknown data within the ranges of examined parameters. Finally, the SHapley Additive exPlanations (SHAP) analysis was implemented to identify the most influential inputs and to quantify their behavior of input variables on the UCS.

This paper presents an experimental study on the evolution of the neutral axis’s depth failure with the ductility at bending on high-strength concrete beams. Nineteen simply supported beams were tested and analyzed. Each beam was loaded by two symmetrical concentrated loads applied approximately at the span’s thirds. The concrete strength and the longitudinal tensile reinforcement ratio vary from beam to beam. As far as the ductility is concerned, ductility indexes were defined in order to characterize such property. The ductility’s influence on the value of the neutral axis’ depth at failure is analyzed and discussed. From the experimental program, the neutral axis’ depth at failure in high-strength concrete beams seams to be a valuable parameter to preview the beam ductility. Therefore, the practical rule of limiting the neutral axis’ depth of the critical sections presented by some codes of practice for normal strength concrete can be extended to high-strength concrete.

Topology Optimisation is a broad concept deemed to encapsulate different processes for computationally determining structural materials optimal layouts. Among such techniques, Discrete Optimisation has a consistent record in Civil and Structural Engineering. In contrast, the Optimisation of Continua recently emerged as a critical asset for fostering the employment of Additive Manufacturing, as one can observe in several other industrial fields. With the purpose of filling the need for a systematic review both on the Topology Optimisation recent applications in structural steel design and on its emerging advances that can be brought from other industrial fields, this article critically analyses scientific publications from the year 2015 to 2020. Over six hundred documents, including Research, Review and Conference articles, added to Research Projects and Patents, attained from different sources were found significant after eligibility verifications and therefore, herein depicted. The discussion focused on Topology Optimisation recent approaches, methods, and fields of application and deepened the analysis of structural steel design and design for Additive Manufacturing. Significant findings can be found in summarising the state-of-the-art in profuse tables, identifying the recent developments and research trends, as well as discussing the path for disseminating Topology Optimisation in steel construction.

Most concrete employs organic phase change materials (PCMs), although there are different types available for more specialised use. Organic PCMs are the material of choice for concrete due to their greater heat of fusion and lower cost in comparison to other PCMs. Phase transition materials are an example of latent heat storage materials (LHSMs) that may store or release thermal energy at certain temperatures. A phase transition occurs when a solid material changes from a solid state to a liquid state and back again when heat is added or removed. It is common knowledge that adding anything to concrete, including PCMs, will affect its performance. The goal of this review is to detail the ways in which PCMs affect certain concrete features. This overview also looks into the current challenges connected with employing PCMs in concrete. The review demonstrates a number of important findings along with the possible benefits that may pave the way for more research and broader applications of PCMs in construction. More importantly, it has been elucidated that the optimum PCM integrated percentage of 40% has doubled the quantity of thermal energy stored and released in concrete. Compared to conventional concrete, the macro-encapsulated PCMs showed thermal dependability, chemical compatibility, and thermal stability due to delaying temperature peaks. Furthermore, the maximum indoor temperature decreases by 1.85 °C and 3.76 °C in the test room due to the addition of 15% and 30% PCM composite, respectively. Last but not least, incorporating microencapsulated PCM has shown a positive effect on preventing freeze-thaw damage to concrete roads.