The state-of-the art paper provides an extensive literature review on the work pertaining to structural health monitoring (SHM) systems used to investigate the structural integrity of highway bridges. The focus of this review is on identifying the SHM research efforts that include damage detection, structural capacity evaluation, and remaining service life estimates on such structures. These efforts have spanned a broad range of data processing methods devoted to tracking changes in structural characteristics for damage detection, codified frameworks enabling structural capacity estimating, and reliability analysis to predict remaining life. Our findings are that a large number of studies considered damage detection by data processing methods, whereas a relatively small number of studies were devoted to the estimation of structural capacities and the remaining service life of bridges. We conclude that the critical gaps include a lack of validated SHM systems that use ambient data to examine design...

The use of phase-change materials (PCM) in concrete has revealed promising results in terms of clean energy storage. However, the negative impact of the interaction between PCM and concrete on the mechanical and durability properties limits field applications, leading to a shift of the research to incorporate PCM into concrete using different techniques to overcome these issues. The storage of clean energy via PCM significantly supports the UN SDG 7 target of affordable and clean energy. Therefore, the present study focuses on three aspects: PCM type, the effect of PCM on concrete properties, and connecting the outcome of PCM concrete composite to the United Nations sustainable development goals (UN SDGs). The compensation of reduction in strength of PCM-contained concrete is possible up to some extent with the use of nanomaterials and supplementary cementitious materials. As PCM-incorporated concrete is categorized a type of building material, the large-scale use of this material will affect the different stages associated with building lifetimes. Therefore, in the present study, the possible amendments of the different associated stages of building lifetimes after the use of PCM-incorporated concrete are discussed and mapped in consideration of the UN SDGs 7, 11, and 12. The current challenges in the widespread use of PCM are lower thermal conductivity, the trade-off between concrete strength and PCM, and absence of the link between the outcome of PCM-concrete composite and UN SDGs. The global prospects of PCM-incorporated concrete as part of the effort to attain the UN SDGs as studied here will motivate architects, designers, practicing engineers, and researchers to accelerate their efforts to promote the consideration of PCM-containing concrete ultimately to attain net zero carbon emissions from building infrastructure for a sustainable future.

Although the steel slit dampers commonly utilized for aseismic design approach can dissipate considerable energy created by the yielding of base materials, large residual deformation may happen in the entire frame structure. After strong external excitation, repair costs will be incurred in restoring a structure to its original condition and to replace broken components. For this reason, alternative recentering devices characterized by smart structures, which mitigate the damage for such steel energy dissipation slit dampers, are developed in this study. These devices, feasibly functioning as seismic restrainers, can be improved by implementing superelastic shape memory alloy (SMA) bending bars in a parallel motion with the steel energy-dissipating damper. The bending bars fabricated with superelastic SMAs provide self-centering forces upon unloading, and accordingly contribute to reducing permanent deformation in the integrated slit damper system. The steel slit dampers combined with the superelastic SMA bending bars are evaluated with respect to inelastic behavior as simulated by refined finite element (FE) analyses. The FE slit damper models subjected to cyclic loads are calibrated to existing test results in an effort to predict behavior accurately. The responses of the proposed slit damper systems are compared to those of the conventionally used slit damper systems. From the analysis results, it is concluded that innovative steel slit dampers combined with superelastic SMA bending bars generate remarkable performance improvements in terms of post-yield strength, energy dissipation, and recentering capability.

Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (M_r) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r²) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted M_r values generally yielded the same distribution and trend as the observed M_r data.

Biomasses are organic materials that are derived from any living or recently-living structure. Plenty of biomasses are produced nationwide. Biomasses are mostly combusted and usually discarded or disposed of without treatment as biomass ashes, which include wood and sugarcane bagasse ashes. Thus, recycling or treatment of biomass ashes leads to utilizing the natural materials as an economical and environmental alternative. This study is intended to provide an environmental solution for uncontrolled disposal of biomass ashes by way of recycling the biomass ash and replacing the soils in geotechnical engineering projects. Therefore, in this study, characteristic tests of wood and sugarcane bagasse ashes that are considered the most common biomass ashes are conducted. The test of chemical compositions of biomass ashes is conducted using energy dispersive X-ray spectroscopy (EDS), and Scanning Electron Microscope (SEM), and heavy metal analysis is also conducted. Engineering behaviors including hydraulic conductivity, constrained modulus and shear modulus are examined. Also, coal fly ash Class C is used in this study for comparison with biomass ashes, and Ottawa 20/30 sands containing biomass ashes are examined to identify the soil replacement effect of biomass ashes. The results show that the particle sizes of biomass ashes are halfway between coal fly ash Class C and Ottawa 20/30 sand, and biomass ashes consist of a heterogeneous mixture of different particle sizes and shapes. Also, all heavy metal concentrations were found to be below the US Environmental Protection Agency (EPA) maximum limit. Hydraulic conductivity values of Ottawa 20/30 sand decrease significantly when replacing them with only 1%–2% of biomass ashes. While both the constrained modulus and shear modulus of biomass ashes are lower than Ottawa 20/30 sand, those of mixtures containing up to 10% biomass ashes are little affected by replacing the soils with biomass ashes.

Stress has an impact, not only on a person’s physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.