Concrete structures when exposed to elevated temperature significantly decline their original properties. High temperatures substantially affect the concrete physical and chemical properties causing significant structural decay and generalized damage impairing the safety and serviceability of the structure. Due to the great importance of concrete behavior at elevated temperatures and under fire, many studies have been conducted on cementitious composites, and the most relevant properties have been studied and evaluated. In particular, fiber-reinforced concrete (FRC) has been a subject of great interest in the last decade due to its superior properties compared to ordinary concrete. Several experimental studies and analytical models have been presented to predict concrete and FRC properties. Among the predictive models, machine learning (ML) tools have shown great merits over other analytical models due to their relative accuracy, generalization abilities, flexible mathematical framework, and cost-effective features. Among the ML, the deep learning (DL) models show remarkable performance when predicting the concrete and the FRC properties at high temperatures because of their ability to deal with more complex nonlinear correlations or difficult regression problems. This review paper presents a pioneering survey of the various ML and DL model implementations predicting concrete and FRC properties at high temperatures. The manuscript aims to establish a solid platform on the state of the art for machine and deep learning prediction of cementitious composites' properties at elevated temperatures. It aims to provide interested researchers with research indications, directions, challenges, recommendations, and future perspectives. 

Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives

Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques

This study explores the potential of utilizing waste marble powder (WMP) instead of cement to develop environmentally friendly high-performance concrete (Eco-HPC). It assesses Eco-HPC's particle packing density, workability, mechanical characteristics, and durability at various stages. High-performance concrete (HPC) mixtures were optimized using a Modified Andreasen and Andersen (MAA) particle packing model to minimize cement content from 550 to 385 kg/m3. The MAA particle packing model demonstrated its ability to enhance and refine mixture microstructures. Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were employed to optimize and predict the mechanical properties and water absorption of modified Eco-HPC blends at 7, 28, and 56 days. As the coefficient of determination indicates, ANN outperformed RSM in predicting Eco-HPC properties across various ages. Substituting cement with WMP, Silica Fume (SF), and Fly Ash (FA) results in a dense microstructure. The mortar containing 20 % WMP and 10 % SF achieves the highest wet packing density at 0.9749 and the lowest void ratio at 0.025, respectively. The formation of CSH in the 70C/20 M−10SF mixture led to a substantial reduction in calcium hydroxide content, indicating enhanced mechanical properties. SEM analysis at 56 days demonstrated significant pozzolanic reactions in the 70C/20 M−10SF mixture, resulting in substantial CSH formation. Furthermore, the embodied CO2 index for 70C/20 M−10SF was reduced to 5.07 kg/MPa/m3, compared to the 100C/0M with value of 8.17 kg/MPa/m3. Economic and environmental assessments reveal enhanced mechanical properties and microstructure when using WMP. This evaluation confirms the effectiveness of 70C/20 M−10SF utilization, paving the way for further advancements in Eco-HPC development. 

Modified particle packing approach for optimizing waste marble powder as a cement substitute in high-performance concrete

This study investigated the importance of applying explainable artificial intelligence (XAI) on different machine learning (ML) models developed to predict the strength characteristics of basalt-fiber reinforced concrete (BFRC). Even though ML is widely adopted in strength prediction in concrete, the black-box nature of predictions hinders the interpretation of results. Among several attempts to overcome this limitation by using explainable AI, researchers have employed only a single explanation method. In this study, we used three tree-based ML models (Decision tree, Gradient Boosting tree, and Light Gradient Boosting Machine) to predict the mechanical strength characteristics (compressive strength, flexural strength, and tensile strength) of basal fiber reinforced concrete (BFRC). For the first time, we employed two explanation methods (Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME)) to provide explanations for all models. These explainable methods reveal the underlying decision-making criteria of complex machine learning models, improving the end user's trust. The comparison highlights that tree-based models obtained good accuracy in predicting strength characteristics yet, their explanations were different either by the magnitude of feature importance or the order of importance. This disagreement pushes towards complicated decision-making based on ML predictions which further stresses (1) extending XAI-based research in concrete strength predictions, and (2) involving domain experts to evaluate XAI results. The study concludes with the development of a "user-friendly computer application" which enables quick strength prediction of basalt fiber reinforced concrete (BFRC). 

Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

The integration of emerging technologies into the construction industry is crucial for the successful execution of technologically sophisticated initiatives. Multiple disciplines of Artificial Intelligence (AI) have undergone advancements in recent times, encompassing automation and prediction. Machine learning (ML), a subset of AI, has been predominantly applied to the domain of prediction. Hence, the focus of this review is on numerous machine learning techniques applied in the mechanical evaluation of concrete. Additionally, the whole life cycle of concrete is examined to promote sustainable practices that minimize negative environmental impacts. The evaluation further examines the challenges and prospective advancements of AI within the construction sector, considering the increasing reliance on technological improvement in the next decades. Hence, the review presents an in-depth analysis of viable machine learning models that optimize for certain strength components to improve the model's performance and accuracy. 

Predictive models in machine learning for strength and life cycle assessment of concrete structures

On the implementation of the interpretable data-intelligence model for designing service life of structural concrete in a marine environment

Unsustainable development in the industry has increased the challenge of proper disposal of industrial by‐products and has caused environmental pollution on a large scale. Furthermore, minimizing the carbon dioxide emissions due to industrial and environmental activities is a crucial challenge to controlling the global warming crisis and climate change. The use of industrial by‐product‐based supplementary cementitious materials (SCMs) can reduce fossil fuel consumption through the reduction in cement production and contribute to suitable waste management. In this research, a meta‐model was developed using integration of the decision tree‐based method named reduced error pruning tree (REPtree) and ensemble approaches (boosting and bagging) to improve the pruning phase for strength simulation of concrete mixtures incorporating industrial by‐products. Then, ensemble multiple learning algorithms were applied to develop a meta‐model using the artificial neural network and Gaussian process regression. A comprehensive experimental‐based database incorporating 3836 records from short‐term to long‐term compressive strength (CS) was collected from the literature. Performance measures present that the proposed ensemble boosting‐REPtree model outperforms other benchmark and ensemble models with a correlation coefficient of 0.960 and root mean square error of 7.884 MPa. Also, uncertainty evaluation was done based on the Monte Carlo simulation algorithm in the modeling process. The FAST and SOBOL methods, as global sensitivity analysis, presented water‐to‐binder (W/B) ratio, curing time, and SCMs to binder ratio (SCM/B) as critical components to estimate the CS of concrete mixtures. The computational simulation of the influential parameters on concrete mixtures incorporating industrial by‐products revealed that the experimental factors yielding the highest CS of the mixtures were calculated as follows: cement content of 584 kg/m3, W/B ratio of 0.17, superplasticizer‐to‐cement ratio of 1.88%, and SCM/B ratio of 0.15.

Toward presenting an ensemble meta‐model for evaluation of pozzolanic mixtures incorporating industrial by‐products

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Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques

On the implementation of the interpretable data-intelligence model for designing service life of structural concrete in a marine environment

Toward presenting an ensemble meta‐model for evaluation of pozzolanic mixtures incorporating industrial by‐products