Concrete structures in cold climates are susceptible to freeze-thaw (f-t) damage, which could lead to structural deterioration and stability issues. Accurate assessment of such damage is crucial for effective maintenance and repair strategies. However, current methods for evaluating concrete f-t damage rely on tedious testing procedures that lack automation and real-time capabilities, potentially delaying the repair process. This study proposes an automatic method to assess f-t damage in concrete by combining piezoelectric-based active sensing and deep learning (DL). Firstly, concrete beams with two different force states were prepared and subjected to different f-t cycles, during which measurements including surface characteristics, mass loss, dynamic elastic modulus and stress wave were performed. The relationship between concrete f-t damage evolution and stress wave signal variation was investigated. The collected signals were then converted into time-frequency maps using continuous wavelet transform (CWT), thereby establishing a CWT image-based dataset. More importantly, an innovative DL model (DSC-ACGRU) that integrates depth-wise separable convolution (DSC), convolutional gated recurrent unit (ConvGRU) and attention mechanism (AM) was developed to automatically extract damage-sensitive feature from CWT images and ultimately predict the degree of concrete f-t damage. Finally, the performances of the proposed DSC-ACGRU model were experimentally evaluated and compared with five machine/deep learning methods. The results show that the proposed model can rapidly and accurately assess the f-t damage degree in concrete and outperforms other learning algorithms, which is significant for identifying potential hazards and repairing damaged concrete. 

Automatic assessment of freeze-thaw damage in concrete structures using piezoelectric-based active sensing approach and deep learning technique

Spatial structure digital twins: Application in intelligent health monitoring of cable dome structures

The cable-strut structures have the features of low stiffness and weak damping, resulting in large vibration response under dynamic excitation. The vibration response of the cable-strut structures can be reduced by active control methods. However, many traditional active control methods need to build a state-space model (SSM) based on finite element model (FEM), and solving the active control force for the high-dimensional SSM is time-consuming. In this paper, the dynamic mode decomposition with control (DMDc) method is proposed to achieve a data-driven reduced-order SSM for active vibration control of cable-strut structures. This proposed method only uses the data of structural response and control force input to build reduced-order SSM and does not rely on the FEM. Linear quadratic regulator (LQR) design using the DMDc-based reduced-order SSM (DMDc-LQR) is more computationally efficient than that using the FEM-based SSM (FEM-LQR). The relationship between the design parameters of the DMDc-LQR controller and the FEM-LQR controller is established, which ensures that the control performance indexes of the DMDc-LQR controller and the FEM-LQR controller are equal. The effectiveness of the proposed DMDc-LQR method is validated using numerical Kiewitt cable dome and tensegrity grid under wind and seismic loads. The results show that the DMDc-LQR controller maintains similar control effect to that of the FEM-LQR controller and has advantage of high computing efficiency as well. In addition, the influence of critical parameters (i.e., data noise and model order) on vibration control effect is comprehensively explored. It is found that increasing the model order is effective to mitigate the noise influence on the DMDc-based reduced-order SSM and ensures the effectiveness of the DMDc-LQR controller. 

Data-driven model reduction approach for active vibration control of cable-strut structures

Optimization method of cable structure demolition driven by digital twin evolution model

This research presents a novel methodology for Deep Reinforcement Learning (DRL) and Bayesian Optimisation of the thermal performance of PCMs in building operations. The developed models utilise a unique and large dataset comprising 1500 building thermal profiles, simulated for various climates and building setups obtained from our industry partners and as open data. PCM-based systems are deployed for thermal insulation of building envelopes to regulate indoor temperature conditions and reduce the need for heating and cooling systems, resulting in enhanced energy efficiency. Through the real-time management of the thermal efficacy of PCMs using the DRL method trained on the large dataset and fine-tuning of the underlying model parameters using Bayesian Optimisation, the optimised system achieves energy saving in heating and cooling load of up to 45 percent, along with the induced reduction in CO2 emission. At the same time, DRL contributes to decreasing the thermal fluctuation in the indoor temperature and keeps it in the narrow range of 1.2 °C in case of high thermal variability scenarios. Currently, the best performance is reported in the literature. This research exemplifies the potential of DRL and Bayesian optimisation in sustainable building. It depicts the applications of advanced intelligent computing algorithms with big building energy data as a novel, robust and superior approach for optimising real-world building energy management systems. The methodology and the improvements in energy savings in thermal and energy management of buildings highlight the novelty and potential benefit of the implemented research as a new intellectual property towards sustainable building design. 

Optimizing the thermal performance of phase change materials in building applications using deep reinforcement learning and Bayesian optimization

A new configuration of Geiger-type cable domes with sliding ridge cables: Computational framework and structural feasibility investigation

The thermal and humidity environment within the early age concrete seriously affects the cement hydration process, which is crucial to the mechanical properties and durability of concrete. It is challenging to estimate the internal relative humidity (IRH) of concrete using conventional theoretical methodologies due to the complicated effect between environmental temperature and relative humidity. In this study, a novel approach based on bidirectional long short-term memory (BiLSTM) is proposed for concrete IRH prediction by directly analyzing the measured data. The proposed approach establishes the IRH prediction models using the BiLSTM network with the optimal hyperparameters estimated by the Bayesian optimization algorithm named Bayes-BiLSTM. The best combination of input for the prediction models is determined using a variety of data types, including the environmental temperature and relative humidity, the internal temperature, and the partial IRH of the concrete. The designed models are applied to predict the measured IRH data from a concrete experiment under different temperature conditions. Three indexes are used to compare the performance of the proposed Bayes-BiLSTM with LSTM and BiLSTM. To further investigate the applicability of Bayes-BiLSTM, the trained networks are applied to measured data from the repeat tests. The prediction results of the two cases demonstrate the effectiveness of the proposed IRH prediction approaches. Model 6 and Model 12 have the best predicting performance for these two cases. The Bayes-BiLSTM outperformed the LSTM and BiLSTM in terms of predicting concrete IRH with lower relative root mean square errors. 

Prediction of internal relative humidity of concrete under different thermal conditions using an enhanced long short-term memory network

A Gaussian processes-based approach for damage detection of concrete structure using temperature-induced strain

Deformation prediction model of large-span prestressed structure for health monitoring based on robust Gaussian process regression

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Papers

A new configuration of Geiger-type cable domes with sliding ridge cables: Computational framework and structural feasibility investigation

Prediction of internal relative humidity of concrete under different thermal conditions using an enhanced long short-term memory network

A Gaussian processes-based approach for damage detection of concrete structure using temperature-induced strain

Deformation prediction model of large-span prestressed structure for health monitoring based on robust Gaussian process regression