Search Results (71,608)

Assembled H-shaped steel strut (AHSS) has been widely applied in deep excavation projects. In this study, the collapse failure of AHSS C1 in a deep excavation project in China was investigated. The collapse of C1 was directly attributed to the settlement of its supporting columns in the mid-span, which was triggered by a nearby pit bottom leakage through an exploration borehole. Then the implementation of the emergency measures and reconstruction works were introduced. Theoretical and numerical pre-assessments confirmed that the reconstructed C1 exhibited adequate safety for strength, in-plane stability and out-of-plane stability, with all steel components and bolts within their safe limits. The good working performance of reconstructed C1 was finally verified through the monitoring results (i.e., strut axial force, soil horizontal displacement, column vertical displacement, road settlement and building settlement) of the foundation pit during the subsequent soil excavation and basement construction. This study is believed to provide references for future excavation projects using AHSS with similar risks. Full article

Ukraine’s reconstruction, estimated at $524 billion over the next decade, presents an unprecedented opportunity to embed circular economy principles into industrial rebuilding, but the financial architecture currently deployed for reconstruction is structurally blind to circular outcomes. This paper examines how digital tools and innovative financing mechanisms can channel investment toward circular industrial reconstruction in Ukraine, drawing on Germany’s National Circular Economy Strategy (NCES, adopted December 2024) as a reference model. A comparative institutional analysis combines a documentary review of Ukrainian reconstruction policy frameworks (Ukraine Plan 2024–2027, RDNA4, Ukraine Facility) and German NCES instruments with the construction of a financing−technology pathway typology. Five pathways are proposed: circular bond issuance with Digital Product Passport integration; blended finance with blockchain impact verification; EU Facility conditionality with AI-driven resource management; war risk insurance with circular construction standards; and SME digitalisation credit with circular economy competency building. Each pathway is assessed against five criteria: investment scale, risk mitigation, circular measurement, digital readiness, and institutional feasibility, and applied to four industrial corridors (Dnipro region, Zaporizhzhia region, Kharkiv region, and Donetsk region). The analysis reveals that no single pathway is sufficient; a layered strategy differentiating by region is required. Digital tools, particularly the Digital Product Passport and blockchain traceability, serve as partial substitutes for institutional trust in post-conflict settings, reducing information asymmetry between investors and project operators. The paper contributes a practically oriented framework at the under-theorised intersection of post-conflict reconstruction finance and circular economy scholarship. Full article

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article

Modern construction extensively utilizes foam concrete (FC) because of its distinct characteristics. However, its application is limited by its low strength properties. Developing high-strength FC by strengthening the matrix with various additives and incorporating various types of fibers into the composition is one of the most rational trends, consistent with the concept of sustainable and environmentally friendly construction. This study explores the impact of diverse polymer fibers on the strength and deformation characteristics of fiber-reinforced foam concrete (FRFC). The concrete’s matrix is strengthened by a composite nanomodifying additive. A FEM model was developed, and experimental studies of the compressive and flexural strength of FRFC were conducted. In the numerical study, the FC matrix is described by the Menetrey-Willam model. Parameter calibration and model verification demonstrated good agreement with experimental data. Experiments and numerical simulations proved that polypropylene fibers enhance compressive strength by as much as 20% and flexural strength by 80%. The stress–strain condition of FRFC was numerically analyzed, considering the influence of steel, carbon, and glass fibers. It was shown that high-modulus polymer fibers quickly lose their adhesive properties and impair the deformation properties of the composite compared to polypropylene fibers. Full article

Renewable energy technologies are becoming more and more relevant in a variety of engineering fields as a result of the move toward low-carbon, sustainable energy systems. Although research has historically concentrated on power generation, it now covers a broad range of applications, including precision agriculture, smart grids, energy storage, healthcare devices, and sustainable buildings. However, existing review studies are often limited to single disciplines or specific technologies, lacking a unified cross-disciplinary perspective that captures the interconnected nature of modern renewable energy systems. This gap motivates the need for a comprehensive review that bridges multiple engineering domains. This review provides a comprehensive synthesis of literature on renewable energy applications in electrical and electronics, computer, environmental, biomedical, architectural, and agricultural engineering. In electrical and electronics engineering, the use of renewable energy sources is largely based on the efficient generation of electricity from natural resources such as solar, wind, and ocean energy. Computer engineering contributes through artificial intelligence (AI), Internet of Things (IoT) architectures, digital twins, and cybersecurity solutions, optimizing energy management. Environmental engineering emphasizes life cycle assessment, carbon footprint reduction, and circular economy strategies. In biomedical engineering, energy harvesting and self-powered devices illustrate micro-scale applications of renewable energy. Architectural engineering integrates renewable systems through building-integrated photovoltaics, net-zero energy designs, and smart building management, while agricultural engineering uses solar-powered irrigation, biomass utilization, agrivoltaic systems, and other sustainable practices. To support a low-carbon future with integrated and sustainable engineering solutions, this study not only highlights innovations within individual fields but also showcases how different disciplines can connect and work together. Overall, the review offers a novel cross-disciplinary framework that advances the understanding of renewable energy systems beyond isolated applications and provides direction for future integrative research. Full article

Although increasing numbers of young people are trying to quit e-cigarettes, personalized tools to support vaping cessation remain limited. We aimed to build a machine learning model to predict individual probability of short-term relapses and identify person-specific barriers to successful cessation. Data were taken from the “Stop Vaping Challenge” smartphone app. We included past 30-day e-cigarette users aged 15–35 years (n = 311) who completed 387 quit challenges. Feature selection minimized number of predictors while maximizing predictive ability. We built multiple GBM survival models with different sets of predictors to predict time to vaping relapse. The five-feature model yielded the best performance (C-index 0.751), thereby was selected as the final model. These five features were: self-confidence in quitting, intention to quit, average e-liquid used per week, time to first vape and mood trend during challenge. We stratified the challenges by the individual relapse risk by 7 days into low-, medium-, and high probability of quit success. This approach can inform tailored quit plans for vaping cessation. SHAP analysis demonstrated individual-level barriers to cessation, which can guide the development of personalized, predictor-centric, adaptive behavioral interventions. However, future research is needed to implement the model in real-world settings and evaluate its effectiveness and generalizability. Full article

Building upon previously developed thin-film modular soft actuators for elongation and deflection, this study develops a modular soft robot for pipeline locomotion, addressing insufficient anchoring capability in confined environments. Conventional inflatable airbags typically expand into spindle-shaped geometries, resulting in limited contact length and reduced effective gripping stability. To overcome this issue, a corrugated thin-film gripping actuator is proposed, in which two high-aspect-ratio sub-airbags are arranged above a compression structure to regulate deformation through geometric constraints. Numerical simulation and experimental evaluation were conducted to investigate contact behavior and locomotion performance. Under an input pressure of 30 kPa, the proposed design achieves a contact length of 46 mm, compared to 37 mm for a conventional three-layer airbag configuration under the same conditions, corresponding to a 24.33% increase in a 10 mm plate-spacing environment. The gripping module is integrated into the modular framework to extend the motion primitives of the soft robot to include anchoring functionality. The results indicate that the corrugated structure effectively suppresses the spindle effect and improves contact effectiveness under compression. These findings demonstrate that structural regulation of thin-film pneumatic actuators provides a feasible strategy for enhancing anchoring performance and locomotion capability of soft robots in confined pipeline environments. Full article

The earthquake of 23 June 2001, Mw 8.4, caused catastrophic damage in the city of Moquegua (Peru), especially in reinforced-concrete educational buildings. In this research, advanced procedures have been used and compared to assess the seismic performance of a new educational building designed under the current Peruvian construction regulations. Two nonlinear static procedures, the capacity spectrum method and an improved procedure based on the equivalent linearization method, have been applied and compared. Damage probabilities for a 475-year-return-period earthquake for the city of Moquegua evidence that the improved procedure based on the equivalent linearization method turns out to be slightly more conservative than the capacity spectrum method. Incremental dynamic analyses, based on 15 seismic events selected according to specific criteria, are taken as reference and complete the building damage assessment. Probabilistic damage matrices are proposed to assess damage using a probabilistic approach, which makes it possible to determine the levels of risk to be assumed in likely post-seismic scenarios and to carry out probabilistic estimates of the impacted population, the expected damage to structures, and the ranges of economic (social and material) costs. These tools assist stakeholders, civil protection and fire departments and the administrations involved in risk management and contingency planning in developing prevention strategies and improving preparedness for natural disasters such as earthquakes. Full article

The construction sector remains a major contributor to global energy consumption and greenhouse gas emissions. Heat loss through building envelopes plays a key role, especially in regions with long heating seasons. Hollow building blocks are widely used due to their low cost and structural simplicity, but their inadequate thermal insulation requires additional layers of insulation, increasing costs and complicating installation. The production of cement and traditional insulation materials is associated with a high carbon footprint and disposal issues, which conflict with sustainable development principles and decarbonization goals. In contrast to previous reviews that primarily address bio-based insulation in general building envelopes or focus on bioaggregates in concrete mixes, this paper specifically targets the application of biomaterials in hollow building blocks. It emphasizes how bio-based loose-fill and bound fillers interact with the peculiar thermo-fluid behavior of hollow cavities, including natural convection, conduction and radiation. The effects on thermal performance (thermal conductivity, U-value of walls) are analyzed, along with selected aspects of mechanical strength and durability. Gaps in long-term data on biodegradation are identified. Recommendations for selecting strategies depending on climate and design are offered, as well as directions for future research, including numerical modeling of thermal conditions. The results highlight the potential of biomodified blocks for creating energy-efficient and environmentally friendly wall systems. Full article

In advanced technology nodes, mixed-cell-height circuit designs have become increasingly prevalent, posing significant challenges for legalization. We first formulate the legalization as a class of variational inequality (VI) problems defined over convex sets and then employ an existing self-adaptive inertial projection and contraction algorithm (SIPCA) to solve it. Building upon this framework, we further propose an improved self-adaptive inertial projection and contraction algorithm (SIPCA_IP) by incorporating the subgradient extragradient technique to enhance convergence efficiency and numerical stability. The proposed method preserves the advantages of projection and contraction schemes for handling VIs with nonsymmetric positive semidefinite system matrices while demonstrating faster convergence and improved robustness compared with the baseline SIPCA. Moreover, a rigorous convergence analysis is established to provide theoretical guarantees for the effectiveness of the proposed method. Numerical experiments demonstrate that the proposed method effectively addresses the mixed-cell-height legalization problem and provides a rigorous and extensible framework for solving related quadratic optimization problems. Full article

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k−ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications. Full article

Radon is a significant indoor air pollutant and a leading cause of lung cancer in non-smokers. While geogenic radon potential is well-documented, the specific contribution of building materials—particularly historic stones and those containing industrial by-products—requires precise in situ characterization to ensure public safety. This study investigates radon activity concentrations and surface exhalation rates across three distinct case studies in Slovakia: a mid-20th-century structure with cinder blocks, a UNESCO-protected Gothic building featuring volcanic andesite, and a historic stone plinth. Continuous radon monitoring and accumulation chamber measurements were employed, integrated with the tracking of meteorological parameters. The results revealed the highest surface exhalation rate in cinder block masonry (8.98 Bq m⁻² h⁻¹), followed by andesite ashlars (7.9 Bq m⁻² h⁻¹) and stone (1.87 Bq m⁻² h⁻¹). A clear correlation was observed between indoor radon levels and barometric pressure, whereas the influence of outdoor temperature appeared negligible. An estimated Activity Concentration Index of 0.30 suggests that the volcanic rock is likely radiologically safe for use as a bulk building material. The study concludes that while specific materials contribute to exhalation, indoor radon stability is primarily governed by barometric variations and the effectiveness of floor barriers against geogenic ingress rather than the masonry itself. Full article

This paper proposes a deep-learning-based scheduling approach for community microgrids that explicitly accounts for building thermal dynamics and customer comfort preferences. Traditional heating, ventilation, and air-conditioning (HVAC) scheduling models are NP-hard and scale poorly, especially for large systems with many buildings. To address this challenge, we develop a dual-encoder deep learning model that predicts building-level HVAC ON/OFF schedules using temporal load and temperature profiles, along with static building thermal parameters. The proposed model is trained in a supervised manner using solutions generated by an optimization-based HVAC scheduling framework, thereby serving as a computationally efficient surrogate for predicting HVAC schedules within a microgrid. The model is trained on samples generated by the optimization-based HVAC scheduling framework and evaluated using precision, recall, and F1-score. The results indicate strong predictive performance. Full article

Last-mile delivery companies can face demand surges where large-volume order requests exceed daily courier capacity. In such cases fast and robust feasibility-first planning becomes more practical and valuable than building optimal routes. This paper proposes a hierarchical, computationally feasible decomposition pipeline that produces shift-feasible clusters under a strict shift-duration limit using travel-time-based duration estimates. While decomposition methods for large-scale VRPs are well established, they typically remain oriented toward route-construction quality within a single operational day or toward balancing customer counts, demand, or Euclidean territory partitions. In contrast, the proposed method targets a different decision problem: rapid feasibility-first strategic capacity planning for one-time extreme demand surges, where the primary requirement is to estimate, within seconds, a conservative upper bound on the number of courier shifts under a strict shift-duration limit. When end-to-end latency is evaluated from raw geographic points, including distance-matrix preparation for monolithic baselines, the proposed pipeline becomes 187 to 1315 times faster than matrix-based monolithic optimization on the common benchmark sizes. Methodologically, the contribution lies in combining (i) topology-preserving spatial linearization with a Hilbert Space-Filling Curve, (ii) adaptive greedy microclustering driven by empirical travel-time quantiles, and (iii) lexicographic dynamic-programming merge that minimizes the number of shifts first and total travel time second. This yields a planning-oriented decomposition mechanism that is distinct from classical route-quality-centered hierarchical VRP approaches. Full article

Building Information Modeling (BIM) supports information integration and coordination across the construction lifecycle, but benefits depend on collaboration that is shaped by the selected project delivery method (PDM). BIM-PDM evidence is difficult to consolidate due to heterogeneous terminology and fragmented, context-specific studies. This scoping review maps which PDMs are addressed in the BIM-related literature and how adequacy is framed. Following PRISMA-ScR, Web of Science and Scopus were searched and 71 studies met the eligibility criteria. Publications increased markedly after 2018 and were geographically concentrated, with the largest shares associated with author affiliations in China, the United Kingdom, Australia, Canada, Malaysia, and the United States. Integrated Project Delivery (IPD) was the most frequently examined (46 studies), followed by Design–Bid–Build (DBB) (29), Design–Build (DB) (29), Public–Private Partnership (PPP) (17), and Engineering, Procurement, and Construction (EPC) (14), while Alliancing, Lean-oriented delivery approaches, and Construction Management were comparatively underrepresented. A temporal analysis indicates a recent shift toward collaborative delivery methods in BIM research. Case-based studies are predominantly situated in public sector projects, with DBB, DB, EPC, and IPD examined across both infrastructure and building contexts, while PPP is limited to infrastructure. The literature is largely focused on design and construction phases, with limited attention to early project stages and operation and maintenance. Results indicate both traditional and relationship-based PDMs are studied in the existing literature, with research framing PDMs that allow for early contractor involvement as most compatible with BIM. Moreover, IPD, DB, and EPC show the best alignment compared to most used traditional DBB methods primarily due to the early involvement of the contractor in the project. EPC and DB achieve this through the allocation of responsibility to the contractor, whereas IPD relies on the early engagement of key participants and the systematic alignment of their objectives. Collaborative and relationship-based approaches are consistently presented as the most suitable for BIM, while DBB tends to constrain BIM benefits because of its fragmented nature. This study contributes by providing a systematic synthesis of BIM-PDM relationships in the scientific literature, identifying the key mechanisms underlying the suitability of different delivery methods for BIM implementation, and offering recommendations for future research based on the identified gaps. Full article