Search Results (228)

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

This research aimed to evaluate the impact of using brick waste powder (BWP) and varying lengths of polyester fibers (PFs) on the performance properties of asphalt concrete (AC) mixtures. BWP was utilized as a replacement for traditional limestone powder (LS) filler, while PFs of three lengths (3 mm, 8 mm, and 15 mm) were introduced. The study employed the response surface methodology (RSM) for experimental design and analysis of variance (ANOVA) to identify the influence of BWP and PF on the selected performance indicators. These indicators included bulk density, air voids, voids in the mineral aggregate, voids filled with asphalt, Marshall stability, Marshall flow, Marshall quotient, indirect tensile strength, wet tensile strength, and the tensile strength ratio. The findings demonstrated that BWP improved moisture resistance and the mechanical performance of AC mixes. Moreover, incorporating PF alongside BWP further enhanced these properties, resulting in superior overall performance. Using multi-objective optimization through RSM-based empirical models, the study identified the optimal PF length of 5 mm in combination with BWP for achieving the best AC properties. Validation experiments confirmed the accuracy of the predicted results, with an error margin of less than 8%. The study emphasizes the intriguing prospect of BWP and PF as sustainable alternatives for improving the durability, mechanical characteristics, and cost-efficiency of asphalt pavements. Full article

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

The accumulation of end-of-life tires and the rapid increase in demolition activities pose significant environmental and waste-management challenges. The redevelopment of construction materials incorporating this waste is a potentially promising strategy for minimizing environmental impact while promoting the principles of a circular economy. This study investigates the performance of self-compacting concrete (SCC) incorporating up to 20% rubber aggregates (sand and gravel) and 40% recycled concrete aggregate (RCA) for non-structural applications. A series of tests was conducted to assess fresh and hardened properties, including flowability, compressive strength, tensile strength, flexural strength, water absorption, and density. The results indicated that increasing RCA content reduced density and compressive strength, while tensile and flexural strengths were only moderately affected. Response surface methodology (RSM), utilizing a Box–Behnken design, was employed to optimize compressive, tensile, and flexural strength responses. Statistical analysis was used to identify the optimal mix proportions, which balance the mechanical performance and sustainability of SCC with recycled components. Mixtures incorporating moderate rubber content—specifically, 5–5.5% sand rubber and 0–6% coarse rubber—and 40% recycled-concrete aggregate (RCA) achieved the highest predicted performance, with compressive strength ranging from 20.00 to 28.26 MPa, tensile strength from 2.16 to 2.85 MPa, and flexural strength reaching 5.81 MPa, making them suitable for sidewalks and walkways. Conversely, mixtures containing higher rubber proportions (5.5–20% sand rubber and 20% coarse rubber) combined with the same RCA level (40%) showed the lowest mechanical performance, with compressive strength between 5.2 and 10.08 MPa, tensile strength of 1.05–1.41 MPa, and flexural strength from 2.18 to 3.54 MPa. These findings underscore the broad performance range achievable through targeted optimization. They confirm the viability of recycled materials for producing environmentally friendly SCC in non-structural applications. Full article

This study aims to enhance the performance of an innovative steel slag–ceramsite foam concrete (SSCFC) to advance sustainable green building materials. An eco-friendly composite construction material was developed by integrating industrial by-product steel slag (SS) with lightweight ceramsite. Employing a three-factor, three-level orthogonal experimental design at a fixed density of 800 kg/m³, 12 mix proportions (including a control group) were investigated with the variables of water-to-cement (W/C) ratio, steel slag replacement ratio, and ceramsite replacement ratio. The governing mechanisms of the W/C ratio, steel slag replacement level, and ceramsite replacement proportion on the SSCFC’s fluidity and compressive strength (CS) were elucidated. The synergistic application of range analysis and analysis of variance (ANOVA) quantified the significance of factors on target properties, and partial least squares regression (PLSR)-based prediction models were established. The test results indicated the following significance hierarchy: steel slag replacement > W/C ratio > ceramsite replacement for fluidity. In contrast, W/C ratio > ceramsite replacement > steel slag replacement governed the compressive strength. Verification showed R² values exceeding 65% for both fluidity and CS predictions versus experimental data, confirming model reliability. Multi-criteria optimization yielded optimal compressive performance and suitable fluidity at a W/C ratio of 0.4, 10% steel slag replacement, and 25% ceramsite replacement. Full article

To reduce the carbon footprint of the concrete industry and promote a circular economy, this study explores the reuse of waste materials such as glass powder (GP) and nitrile rubber (NR) fibres in concrete. However, the inclusion of these waste materials results in lower compressive strength compared to conventional concrete, limiting their application to non-structural elements. To overcome this limitation, this study adopts the concept of confined concrete by developing concrete-filled steel tube (CFST) stub columns. In total, twelve concrete mix variations were developed, with and without steel tube confinement. GP was utilised at replacement levels of 10–30% by weight of cement, while NR fibres were introduced at 0.5% and 1% by volume of concrete. The findings demonstrate that the incorporation of GP and NR fibres leads to a reduction in compressive strength, with a compounded effect observed when both materials are combined. Steel confinement within CFST columns effectively mitigated the strength reductions, restoring up to 17% of the lost capacity and significantly improving ductility and energy absorption capacity. All CFST columns exhibited consistent local outward buckling failure mode, irrespective of the concrete mix variations. A comparison with predictions from existing design codes and empirical models revealed discrepancies, underscoring the need for refined design approaches for CFST columns incorporating sustainable concrete infill. This study contributes valuable insights into the development of eco-friendly, high-performance structural systems, highlighting the potential of CFST technology in facilitating the adoption of waste materials in the construction sector. Full article

The following investigation presents concrete cement obtained with the Viterbo O’Reilly Diaz method, introduced to quantify the concrete mixture by using an aggregate gradation method. This research uses this procedure to decrease the amount of cement in the mix, thus reducing the CO₂ footprint and production costs, which directly impact the environmental and economical sustainability of the material. The formulations used structural and general use Portland cements. As aggregates, fine sand and 3/4” gravel were included. Several characterization techniques were used, including granulometry testing for the aggregates, compression strength testing for the concrete samples, and granulometry testing for the raw materials. Compressive tests were conducted on samples after 28 days of curing, while scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS) was used to understand the microstructure. The results revealed the optimal amounts of water, cement, and aggregates. Combinations of fine and coarse aggregates were determined as well. The main novelty in this manuscript is the use of the Viterbo O’Reilly mix design method to innovatively enhance concrete mixes by analyzing material properties and behavior in detail, an unexplored method in the literature. This research considers not only strength but also durability and workability, using mathematical tools for data analysis. This data-driven approach ensures effective aggregate gradation towards sustainability when compared to other traditional methods. Full article

Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R² = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO₂ emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality. Full article

Concrete structures increasingly face dynamic loading conditions, such as seismic events, vehicular traffic, and environmental vibrations, necessitating enhanced energy dissipation capabilities. The damping ratio, a critical parameter quantifying a material’s ability to dissipate vibrational energy, is typically low in conventional concrete, prompting extensive research into strategies for improvement. This review comprehensively explores the impact of advanced concrete types—such as Engineered Cementitious Composites (ECCs), Ultra-High-Performance Concrete (UHPC), High-Performance Concrete (HPC), and polymer concrete—on enhancing the damping behavior. Additionally, key mix design innovations, including fiber reinforcement, rubber powder incorporation, and aggregate modification, are evaluated for their roles in increasing energy dissipation. External factors, particularly curing conditions, are also discussed for their influence on the damping performance. The findings consolidate experimental and theoretical insights into how material composition, mix design, and external treatments interact to optimize dynamic resilience. To guide future research, this paper identifies critical gaps including the need for multi-scale numerical simulation frameworks, standardized damping test protocols, and long-term performance evaluation under realistic service conditions. Advancing work in material innovation, optimized mix design, and controlled curing environments will be essential for developing next-generation concretes with superior vibration control, durability, and sustainability. These insights provide a strategic foundation for applications in seismic-prone and vibration-sensitive infrastructure. Full article

Historic cities across the globe have experienced profound changes in their spatial and functional characteristics over time, and the historic core of Patan, Nepal, is no exception. The area surrounding Patan Durbar Square was designated as a UNESCO World Heritage Site in 1979. Between 2003 and 2007, the Kathmandu Valley was placed on UNESCO’s List of World Heritage in Danger, largely due to various factors, including the rapid and unsympathetic transformation of its buffer zone. This study focuses on the Nagbahal neighborhood, a culturally significant locality within this buffer area, to explore a community-rooted and sustainable approach to conservation. Employing a mixed-methods research design, the study integrates qualitative and quantitative data gathered through interviews and surveys of native residents. It investigates the drivers and impacts of changes in the function, ownership, and physical form of traditional residential buildings, and assesses whether these changes align with principles of sustainable heritage conservation—social, cultural, economic, and environmental. While challenges persist, including the proliferation of reinforced concrete structures and limited enforcement of heritage policies, the findings reveal that Nagbahal remains resilient due to strong local traditions, active religious institutions, and cohesive social practices. The study offers transferable lessons for sustainable conservation in living heritage buffer zones globally. Full article

The durability and fire resilience of concrete structures are increasingly critical in modern construction, particularly under elevated-temperature exposure. With this context, the current study explores the thermal and microstructural characteristics of geopolymer-based ultra-high-performance concrete (G-UHPC) incorporating quartz powder (QP) and silica fume (SF) after exposure to elevated temperatures. SF was used at 15% and 30% to partially replace the precursor material, while QP was used at 25%, 30%, and 35% as a partial replacement for fine sand. The prepared specimens were exposed to 200 °C, 400 °C, and 800 °C, followed by air cooling. Mechanical strength tests were conducted to evaluate compressive and flexural strengths, as well as failure patterns. Microstructural changes due to thermal exposure were assessed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Among the prepared mixtures, the 30SF35QP mixture exhibited the highest compressive strength (156.0 MPa), followed by the 15SF35QP mix (146.83 MPa). The experimental results demonstrated that G-UHPC underwent varying levels of thermal degradation across the 200–800 °C range yet displayed excellent resistance to thermal spalling. At 200 °C, compressive strength increased due to enhanced geopolymerization, with the control mix showing a 29.8% increase. However, significant strength reductions were observed at 800 °C, where the control mix retained only 30.8% (32.0 MPa) and the 30SF25QP mixture retained 28% (38.0 MPa) of their original strengths. Despite increased porosity and cracking at 800 °C, the 30SF35QP mixture exhibited superior strength retention due to its denser matrix and reduced voids. The EDS results confirmed improved gel stability in the 30% SF mixtures, as evidenced by higher silicon content. These findings suggest that optimizing SF and QP content significantly enhances the fire resistance and structural integrity of G-UHPC, providing practical insights for the design of sustainable, high-performance concrete structures in fire-prone environments. Full article

In order to improve the durability of loess-based composite coal gangue porous planting concrete (LCPC), the effects of fly ash and slag powder content on the durability and microstructure of LCPC were studied. In this paper, fly ash and slag powder were mixed into LCPC, and freeze-thaw cycle and dry-wet cycle tests were carried out. The compressive strength, dynamic elastic modulus, and mass change were used as evaluation indices to determine the optimal mix ratio for LCPC durability. Scanning electron microscopy (SEM) was performed, and the experimental design was carried out with the water–cement ratio, fly ash, and slag powder content as variables. The microstructure characteristics of LCPC were analyzed. The results show that the maximum number of freeze-thaw cycles can reach 35 times and the maximum number of dry-wet cycles can reach 50 when 5% fly ash and 20% slag powder are used. With an increase in the water-cement ratio, the skeleton of the loess gradually became complete, and its structure became more compact. In the micro-morphology diagram, the mixed fly ash and slag powder particles are not obvious, but with an increase in dosage, the size of the cracks and pores gradually decreases. The incorporation of fly ash and slag powder can play a positive role in the durability of LCPC and improvement of its microstructure. The results of this study are crucial for improving the application performance of ecological restoration, soil improvement, and long-term stability of structures, and can provide a scientific basis for the sustainable development of environmentally friendly building materials. Full article

This study aimed to optimize the performance of pervious concrete (PC) while promoting sustainability using recycled concrete aggregates (RCAs), styrene butadiene rubber (SBR) waste, and silica fume (SF). The mixtures were developed using the Taguchi approach with four mix design factors, each at three levels: the water-to-binder ratio (w/b), RCA replacement percentage by weight of natural aggregates, the cement substitution rate with SF, and the SBR addition rate by binder mass. Thus, a total of nine mixes were prepared and tested for density, porosity, permeability, compressive strength, splitting tensile strength, abrasion resistance, and resistance to freezing and thawing. The results revealed that incorporating RCA and SBR decreased density and compressive strength but increased porosity and permeability. The performance of PC enhanced with SF addition and reduced w/b. TOPSIS was then employed to find the optimum mixture design proportions by considering multiple performance criteria. The results indicated that a high-performing sustainable PC mixture, with enhanced strength and durability characteristics, was formulated with a w/b ratio of 0.30, 25% RCA, 5% SF replacement, and 4% SBR addition. Full article

Geopolymers (GPs), as promising alternatives to ordinary Portland cement (OPC)-based concrete, have gained interest in the last 20 years due to their enhanced mechanical properties, durability, and lower environmental impact. Synthesized from industrial by-products such as slag and fly ash, geopolymers offer a sustainable solution to waste management, resource utilization, and carbon dioxide reduction. However, similarly to OPC, geopolymers exhibit brittle behavior, and this characteristic defines a limit for structural applications. To tackle this issue, researchers have focused on the characterization, development, and implementation of fiber-reinforced geopolymers (FRGs), which incorporate various fibers to enhance toughness, ductility, and crack resistance, allowing their use in a wide range of structural applications. Following a general overview of sustainability considerations, this review critically analyzes the structural performance and capability of geopolymers in structural repair applications. Geopolymers demonstrate notable potential in new construction and repair applications. However, challenges such as complex mix designs, the availability of alkaline activators, curing temperatures, fiber matrix compatibility issues, and limited standards are restricting its large-scale adoption. The analysis and consolidation of an extensive dataset would support the viability of geopolymer as a durable and sustainable alternative to what is currently used in the construction industry, especially when fiber reinforcement is effectively integrated. Full article

This study presents a performance-based concrete mix design methodology rooted in the paste–aggregate binary framework, aiming to reduce binder content while ensuring optimal workability and strength. We found that inter-particle spacing (SPT) and paste rheology jointly govern fresh concrete behavior, with slump increasing nonlinearly with SPT and a critical transition zone around 20–35 µm; paste yield stress controls slump, while plastic viscosity governs segregation resistance. A two-level strength model was developed to predict concrete strength from paste properties based on compactness and hydration (R² = 0.90). Fixing SPT at 25 µm was identified as optimal for achieving balanced flowability with minimal paste volume. This approach effectively decouples aggregate packing optimization from paste calibration, offering a physically interpretable and practical framework for designing sustainable, low-carbon, and low-shrinkage concrete. Full article