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•Critical review of techniques to integrate PCM in construction materials provided.•Methods of PCMs integration are scrutinized.•Risk of leakage of PCM is challenge for implementing ...PCM in construction materials.•Recommendations of PCM integration methods for various applications are provided.•Knowledge gaps are identified and future research needs are outlined.
Applications of phase change materials (PCMs) have become of great interest in recent years owing to beneficial effects on the thermal, mechanical and durability properties of construction and pavement materials. PCMs can alter the thermal mass and thermal inertia of building materials, thus enhancing thermal energy storage. The effects of PCMs on cement hydration, thermal stress and shrinkage of concrete have stimulated further applications. Despite various virtues of PCMs in construction and pavement materials, their drawbacks still need concerted research efforts. Among the fundamental problems of PCMs is their risk of leakage in the melted state. Hence, several techniques have been proposed to mitigate this problem. The present study examines potential methods of incorporating PCMs into building materials, including microencapsulation, macro-encapsulation, shape-stabilization, and porous inclusion. A critical analysis of PCM applications and stabilization materials and methods in concrete is provided, hence identifying practical recommendations, research needs and current knowledge gaps.
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•Largest available dataset of cementitious composites integrating PCM created.•ML algorithms predicted strength of PCM integrated cementitious composites.•Feature importance of models ...is compared with experimental evidence.•Needed research on PCM microcapsules and concrete technology is identified.
Incorporating phase change materials (PCMs) into cementitious composites has recently attracted paramount interest. While it can enhance thermal characteristics and energy storage, compressive strength can be decreased. Thus, accurate prediction of the effect of PCM addition on compressive strength is crucial. However, a predictive model for this purpose using physical or chemical features is not feasible at this stage. Thus, machine learning is used for the first time herein to predict the compressive strength of PCM-integrated cementitious composites. A dataset of 154 cement-based mixtures incorporating PCM microcapsules was assembled. Various machine learning regression algorithms including random forest, extra trees, gradient boosting, and extreme gradient boosting were tuned and their prediction accuracy was assessed using several metrics. The models achieved superior prediction accuracy. Exploiting powerful machine learning models to examine the harvested experimental data could provide insights into materials science aspects of this problem and identify pertinent knowledge gaps and needed future research.
The present study explores the potential application of unmanned aerial vehicle (UAV) Infrared Thermography for detecting subsurface delaminations in concrete bridge decks, which requires neither ...traffic interruption nor physical contact with the deck being inspected. A UAV-borne thermal imaging system was utilized to survey two in-service concrete bridge decks. The inspection process involved the acquisition of thermal images via low altitude flights using a high resolution thermal camera. The images were then enhanced and stitched together using custom developed codes to create a mosaic thermal image for the entire bridge deck. Image analysis based on the k-means clustering technique was utilized to segment the mosaic and identify objective thresholds. Hence, a condition map delineating different categories of delamination severity was created. The results were validated using data generated by other non-destructive testing technologies on the same bridge decks, namely hammer sounding and half-cell potential testing. The findings reveal that UAV with high-resolution thermal infrared imagery offers an efficient tool for precisely detecting subsurface anomalies in bridge decks. The proposed methodology allows more frequent and less costly bridge deck inspection without traffic interruption. This should enable rapid bridge condition assessment at various service live stages, thus effectively allocating maintenance and repair funds.
•Unmanned aerial vehicle was successfully used in bridge deck condition assessment.•Infrared camera mounted on UAV could delineate delaminations in bridge decks.•Results are demonstrated via two case studies on two full-scale bridge decks in service.•Results of UAV-borne IRT were confirmed using hammer sounding and half-cell potential.
•Empirical models for concrete mechanical strength are inaccurate and cannot accommodate new input parameters.•Machine learning models are more accurate, flexible and can be retrained with updated ...databases.•Advantages and shortcomings of ML models identified model performance is compared.•Recommendations for selecting suitable model are made based on review.•Knowledge gaps and needed future research are identified.
Accurate prediction of the mechanical properties of concrete has been a concern since these properties are often required by design codes. The emergence of new concrete mixtures and applications has motivated researchers to pursue reliable models for predicting mechanical strength. Empirical and statistical models, such as linear and nonlinear regression, have been widely used. However, these models require laborious experimental work to develop, and can provide inaccurate results when the relationships between concrete properties and mixture composition and curing conditions are complex. To overcome such drawbacks, several Machine Learning (ML) models have been proposed as an alternative approach for predicting the mechanical strength of concrete. The present study examines ML models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms. The application of each model and its performance are critically discussed and analyzed, thus identifying practical recommendations, current knowledge gaps, and needed future research.
•Database comprising 713 data records on carbonation of recycled aggregate concrete was created.•GBRT model accurately predicts carbonation depth of recycled aggregate concrete incorporating ...different SCMs.•GBRT model outperforms existing models and mathematical formulations.•New model captures significance of influential parameters on carbonation depth.
While recycled aggregates and supplementary cementitious materials have often been hailed for enhancing concrete sustainability, their effects on the resistance of concrete to carbonation has been controversial. Thus, deploying robust machine learning tools to overcome the lack of understanding of the implications of incorporating such sustainable materials is of paramount importance. Accordingly, this study proposes a gradient boosting regression tree (GBRT) model to determine the carbonation depth of recycled aggregate concrete incorporating different mineral additions, including metakaolin, blast furnace slag, silica fume, and fly ash. For this purpose, a database comprising 713 pertinent experimental data records was retrieved from peer-reviewed publications and used for model development and testing. Furthermore, predictions of the GBRT model were compared with calculations of available mathematical formulations to determine the carbonation depth in concrete. The results demonstrate that the machine learning methodology outperformed all the mathematical models considered in this study. The GBRT proved to be a robust tool that could be used to provide an insight into the resistance of concrete to carbonation and could be extended to predicting other features of concrete incorporating diverse recycled materials.
Falling behind schedule and having discrepancy between the as-built and designed baseline plans are unfavourable events that often occur in construction projects. Hence, real-time progress tracking ...and monitoring of construction components remains a vital part of project management and is critical to achieving project objectives. Yet manual approaches for progress tracking lack the required accuracy for integration with other construction interfaces. Conversely, automatic progress tracking can result in timely detection of potential time delays and construction discrepancies and directly supports project control decision-making. This paper examines different technologies of automated and electronic construction data collection. In particular, enhanced IT, geo-spatial, 3D imaging, and augmented reality technologies have recently achieved significant advances in this field. Each of these technologies is discussed herein in terms of its advantages and limitations. Comparisons of such technologies to identify various trends concerning their applicability in real-time data acquisition of construction projects are made, along with recommendations for their suitability in different projects. This should assist construction stakeholders in choosing appropriate tools to enhance time and cost effectiveness and achieve better control and more effective decisions during construction. It is also hoped that this review will stimulate further research on and development of these technologies.
•Main automated and electronic construction data collection technologies are examined.•Each technology is discussed in terms of its advantages and limitations.•Recommendations for suitability of technologies in different projects are formulated.•Findings can assist in better control and enhanced decisions during construction.•This review will stimulate further research on and development of these technologies.
Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies ...in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.
There have been abundant experimental studies exploring ultra-high-performance concrete (UHPC) in recent years. However, the relationships between the engineering properties of UHPC and its mixture ...composition are highly nonlinear and difficult to delineate using traditional statistical methods. There is a need for robust and advanced methods that can streamline the diverse pertinent experimental data available to create predictive tools with superior accuracy and provide insight into its nonlinear materials science aspects. Machine learning is a powerful tool that can unravel underlying patterns in complex data. Accordingly, this study endeavors to employ state-of-the-art machine learning techniques to predict the compressive strength of UHPC using a comprehensive experimental database retrieved from the open literature consisting of 810 test observations and 15 input features. A novel approach based on tabular generative adversarial networks was used to generate 6513 plausible synthetic data for training robust machine learning models, including random forest, extra trees, and gradient boosting regression. While the models were trained using the synthetic data, their ability to generalize their predictions was tested on the 810 experimental data thus far unknown and never presented to the models. The results indicate that the developed models achieved outstanding predictive performance. Parametric studies using the models were able to provide insight into the strength development mechanisms of UHPC and the significance of the various influential parameters.
•Train on synthetic – test on real philosophy solved problem of limited experimental data base.•TGAN allowed to create reliable synthetic data for model training.•Bayesian optimization algorithm ...using synthesized data optimized new shear strength equation for FRP reinforced beams.•Proposed soft computing approach vastly outperformed existing design code provisions and theoretical models.•Approach could be extended to multiple other applications in composite materials design and engineering.
Machine learning algorithms have emerged as a powerful technique to predict the engineering properties of composite materials and structures where traditional statistical methods have resulted in poor accuracy and high uncertainty. However, the lack of reliable and comprehensive experimental data hinders developing high-throughput computational models. To mitigate such a limitation, this study deploys the state-of-the-art tabular generative adversarial network (TGAN) to generate synthetic data for training generalized ML models. ‘Train on Synthesized - Test on Real’ approach was used to pioneer a novel framework for predicting the shear capacity of FRP-reinforced concrete beams. Accordingly, the models were trained using 8816 synthesized design data and tested using 304 real experimental data. The TGAN approach exhibited tremendous potential in generating credible data for training robust machine learning models, achievingR2of 0.96 when tested on the entire experimental dataset. Furthermore, a Bayesian optimization was performed on the extensive synthesized data to propose a new predictive shear strength equation. Results demonstrate that the proposed design model attained superior accuracy and vastly outperformed both existing design code provisions and empirical and theoretical models in the literature. The TGAN technique could transcend the lack of available experimental datasets in engineering problems via synthetizing numerous plausible data points to enhance the prediction accuracy and generalization ability of machine learning models.
•Novel ML model proposed for predicting behavior of RC columns under blast.•Large dataset for FRP RC columns under blast was compiled.•Statistical metrics indicate that developed model achieved ...superior accuracy.•Feature importance analyses agreed with experimental and numerical studies.•Considering simplicity, speed and accuracy, new model is strong contender.
Considering the risk of exposure to blast and explosive loading, reinforced concrete structures are prone to experiencing partial or total progressive collapse initiated by column failures. Therefore, understanding and predicting the structural response of columns subjected to blast loading fosters proactive measures that could mitigate life and economic losses. The present study introduces a machine learning model to predict the maximum displacement of reinforced concrete columns exposed to blast loading using thirteen features pertaining to imperative column and blast properties. The dataset used in this study consists of 420 data examples retrieved from existing experimental, numerical and analytical studies in the open literature. The model was developed using ensemble tree-based algorithms and was validated through statistical performance metrics, numerous comparisons to existing methods, and feature importance analyses. Additionally, a critical analysis was conducted to assess the importance of features in both near-field and far-field blast exposures. The practical use of the proposed model, along with recommendations for model improvements were discussed. Overall, the usage of tree ensemble algorithms for the proposed model achieved very high prediction performance, resulting in MAE of 3.63 mm, MAPE of 13.31%, R2 of 97.4%, and VEcv of 96.83%, while displaying robust ability to identify correlations between influential parameters and the corresponding response.
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