Coal gangue, as a predominant solid byproduct of the global coal industry, poses severe environmental challenges because of its massive accumulation and low utilization rate. This review systematically synthesizes and analyzes published experimental and analytical studies on the dual-pathway utilization of coal gangue in concrete, including Pathway 1 (aggregate substitution) and Pathway 2 (cementitious activity activation). While the application of coal gangue aggregates is traditionally limited by their inherent high porosity and lower mechanical strength than those of natural aggregates, this review demonstrates that performance barriers can be effectively overcome. Through multiscale modification strategies—including surface densification, biological mineralization (MICP), and matrix synergy—the interfacial defects are significantly mitigated, allowing for feasible substitution in structural concrete. Conversely, for the mineral admixture pathway, controlled thermal activation is identified as a key process to optimize the phase transformation of kaolinite, thereby significantly enhancing pozzolanic reactivity and long-term durability. According to reported studies, the partial replacement of natural aggregates or cement with coal gangue can reduce CO₂ emissions by approximately tens to several hundreds of kilograms per ton of coal gangue utilized, depending on the substitution level and activation strategy, highlighting its considerable potential for carbon reduction in the construction sector. Nevertheless, challenges related to energy-intensive activation processes and variability in raw gangue composition remain. These limitations indicate the need for future research focusing on low-carbon activation technologies, standardized classification of coal gangue resources, and long-term performance validation under realistic service environments. Based on the synthesized literature, this review discusses hierarchical utilization concepts and low-carbon activation approaches as promising directions for promoting the sustainable transformation of coal gangue from an environmental liability into a carbon-reduction asset in the construction industry.

With the global concept of sustainable development gaining widespread acceptance, the resource utilization of solid waste has become an important research direction in the field of building materials. Geopolymer concrete (GPC), especially solid waste-based geopolymer concrete (SWGPC) prepared using various industrial solid wastes as precursors, has gradually become a frontier in green building material research due to its low carbon footprint, high strength, and excellent durability. However, the rapid expansion of literature calls for a systematic review to quantify the knowledge structure, evolution, and emerging trends in this field. Based on two thousand and thirty-nine (2039) relevant articles indexed in the Web of Science Core Collection database between 2008 and 2025, this study employs bibliometric methods and visualization tools such as VOSviewer and CiteSpace to systematically construct a knowledge map of this field. The research comprehensively reveals the developmental trajectory, research hotspots, and frontier dynamics of SWGPC from multiple dimensions, including publication trends, geographical and institutional distribution, mainstream journals, keyword clustering, and burst word analysis. The results indicate that the field has entered a rapid development stage since 2016, with research hotspots focusing on the synergistic utilization of multi-source solid waste, optimization of alkali-activation systems, enhancement of concrete durability, and environmental impact assessment. In recent years, the introduction of emerging technologies such as machine learning, 3D printing, and nano-modification has been driving a paradigm shift in research. This systematic analysis not only clarifies research development trends but also provides a theoretical basis and decision-making support for future interdisciplinary integration and engineering practice transformation.

This paper presents an experimental study on the dynamic strength of concrete by using a split Hopkinson pressure bar. The stress–strain relationship and fragmentation degree of concrete were analyzed. The change process of the incident energy, reflection energy, transmission energy and consumption energy of concrete was calculated. The corresponding relationship between the variation of each energy and the stress state of concrete was studied. The law of energy evolution during the concrete fracture process was determined and the mechanism of concrete dynamic strength increase was revealed from the perspective of energy. The results show that the higher the strain rate, the higher the fragmentation degree of concrete, the smaller the grain diameter of fragments, the easier cracks are to pass directly through the aggregate, and the more regular the fragment shape. The change process of increasing amplitude of concrete consumption energy can reflect four mechanical states of concrete: stress increase, stress slow releasing, stress rapid releasing, and return-to-zero stress. Since the increase in reflected energy does not increase immediately with the increase in strain rate, it leads to the hysteresis of energy release in concrete, resulting in an increase in the dynamic strength of concrete.

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence.

This paper determines the best design parameters and uses conditions of lightweight aggregate concrete panels containing microencapsulated phase change materials (MPCM-LWAC panels). The main work of this paper includes the followings: (1) The fundamental properties (dry density, thermal conductivity, and specific heat capacity) of MPCM-LWAC were researched to reveal the effect of MPCM dosage on these properties. (2) A model test was carried out to quantify the effect of MPCM dosage on the thermal response of the MPCM-LWAC panel exposed to realistic climate conditions. (3) The numerical simulation was conducted to investigate the effect of MPCM dosage, panel thickness, and outdoor temperature conditions on the thermal response of the MPCM-LWAC panel, which helps to determine its optimum design parameters and use condition. The results showed that the incorporation of MPCM results in lower dry density and thermal conductivity of MPCM-LWAC but higher specific heat capacity. The more MPCM dosage in the MPCM-LWAC panel with a thickness of 35 mm, the lower the energy demand to keep a comfortable interior temperature. Most notably, when the panel thickness exceeds 105 mm, the MPCM-LWAC panel with 5% MPCM only delays the peak temperature. Moreover, the optimal use condition for MPCM-LWAC panels is an average outdoor temperature of 25 °C, which makes the energy demand attain a minimum.

To achieve more accurate prediction of the potential failure location and to conduct a deeper analysis of the failure mechanism of concrete constructions, it is critical to probe the evolution process of internal meso-cracks that bear various intensities of load. While a computer Tomography (CT) test provides a non-destructive detection technique for obtaining the internal meso-damage state of concrete, traditional image processing and Digital Image Correlation (DIC) are ineffective in extracting meso-damage information from concrete CT images. On the other hand, by observing the shape change law of concrete’s internal holes under load, it is proposed to use the hole roundness and area fraction formula, developed based on the stereology principle and morphology, to characterize and predict the potential failure location. Four features particularly addressed include the CT image as a whole, image equal partitioning, crack and non-crack areas, and representative holes. The approach is to explore the variation law of critical hole shape parameters, especially the hole roundness under different loading stages, and analyze the relationship between the change in hole shapes and the final macro-crack positions. It is found that compared with the average area fraction, the average hole roundness value of cross section images is more sensitive to the change in stress. In both uniform partitioning and non-uniform partitioning, the average hole roundness value near the final macro-crack location exhibits an increase trend with the stress, while the smoothing effect caused by the hole roundness averaging always exists. Near the final macro-crack location, the roundness of each individual hole is positively associated with the stress, while away from the final macro-crack location such a relation may not be observed. This trend expounds the evolution process of meso-damage in concrete, and the finding can be used to predict the accurate locations of macro-cracks.