This paper proposes a portfolio construction framework designed to remain robust under estimation error, non-stationarity, and realistic trading constraints. The methodology combines dynamic asset eligibility, deterministic rebalancing, and bounded multi-factor tilts applied to an equal-weight baseline. Asset eligibility is formalized as a state-dependent constraint on portfolio construction, allowing factor exposure to adjust endogenously in response to observable market conditions such as liquidity, volatility, and cross-sectional breadth. Rather than estimating expected returns or covariances, the framework relies on cross-sectional rankings and hard structural bounds to control concentration, turnover, and fragility. The resulting approach is fully algorithmic, transparent, and directly implementable. It provides a robustness-oriented alternative to parametric optimization and unconstrained multi-factor models, particularly suited for long-horizon allocations where stability and operational feasibility are primary objectives.
This study presents a novel integrative review of 329 review articles on sustainable buildings from 2015 to 2025, combining quantitative bibliometrics with qualitative insights to map the field’s evolution and pinpoint critical future pathways. Seven core research themes were identified in this study: (1) material and advanced construction technologies, (2) energy efficiency and renewable energy systems, (3) digitalization and smart technologies, (4) policy, standards, and certification, (5) sustainable design and optimization, (6) stakeholder and socio-economic factors, (7) other (cross-cutting) topics. Key findings reveal a surge in publications post-2020, driven by global net-zero commitments, with China, Australia, and Hong Kong leading research output. Innovations in low-carbon materials (e.g., hemp concrete, geopolymers), artificial intelligent (AI)-driven energy optimization, and digital tools (e.g., building information modeling (BIM), internet of things (IoT)) dominate recent advancements. However, challenges persist, including policy fragmentation, scalability barriers for sustainable materials, and socio-economic disparities in green building adoption. The study proposes a unique future research framework emphasizing nanotechnology-enhanced materials, interpretable AI models, harmonized global standards, and inclusive stakeholder engagement. This review provides actionable recommendations to bridge gaps between technological innovation, policy frameworks, and practical implementation in sustainable construction.
Additive manufacturing has recently become popular and more cost-effective for building construction. This study presents a prospective life cycle assessment (LCA) of 3D-printed foamed geopolymer composites (3D-FOAM materials) incorporating construction and demolition waste. The materials were developed using fly ash, slag, sand, and a foaming agent, with recycled clay brick waste (CBW) and autoclaved aerated concrete waste (AACW) added as alternative raw materials. The material formulations were evaluated for their compressive strength and thermal conductivity to define two functional units that reflect structural and thermal performance. A prospective life cycle assessment (LCA) was conducted under laboratory-scale conditions using the ReCiPe 2016 method. Results show that adding CBW and AACW reduces environmental impacts across several categories, including global warming potential and ecotoxicity, without compromising material performance. Compared to conventional wall systems, the 3D-FOAM materials offer a viable low-impact alternative when assessed on a functional basis. These findings highlight the potential of integrating recycled materials into additive manufacturing to support circular economy goals in the construction sector.
Kamal Thapa Kunwar Magar, Yuvraj Paudel, M. Gautam
et al.
Reinforced concrete is a commonly used construction material in the modern age; however, premature corrosion of the embedded steel poses a significant challenge. This corrosion can lead to the premature deterioration of structures, including buildings, pillars, bridges, and drainage systems. This study evaluates the corrosion risk of fifty-three steel-reinforced concrete infrastructures (S-RCIs) in Butwal Sub-Metropolitan City, Nepal, using the corrosion potential mapping (CPM) technique. The CPM is a non-destructive, cost-effective electrochemical method that complies with ASTM C876-22b standards. It measures in-situ open-circuit potential (OCP) values of the S-RCIs) to qualitatively categorize the probability of corrosion into three levels: low corrosion risk (LCorR, i.e., 90%). The findings indicate that the roof samples of residential buildings predominantly fall into the low-risk category, suggesting satisfactory durability. In contrast, fencing pillars, bridges, and drainage pipes show a high likelihood of corrosion, with OCP values indicating a probability of over 90%. Furthermore, the study emphasizes that structures exhibiting visible cracks, signs of delamination, and prolonged exposure to moisture are significantly more susceptible to reinforcement corrosion.
Abstract The current Korean Standard KDS 14 has adopted a strain-based shear strength model for evaluating the punching shear strength of slab–column connections reinforced with steel rebars. Thus, this study evaluated the applicability of the KDS 14 design model for slab–column connections reinforced with FRP rebars. The KDS 14 model was improved by modifying the equation for determining the depth of the compression zone, taking into account the material characteristics of FRP reinforcement. The modified KDS 14 model was evaluated by conducting a comparative analysis with existing design codes over a comprehensive database of 150 interior FRP-reinforced interior slab–column specimens with and without shear reinforcement. The results indicated that the modified KDS 14 model provided promising performance over various design parameters by exhibiting a similar scatter and conservatism compared to the JSCE 2007 and CSA codes with a COV of approximately 15%, while showing better correlation with the dataset than most existing design codes. In addition, a parametric analysis was conducted to investigate the primary design parameters that affected the punching shear stress capacity at the critical section of FRP-reinforced slab–column connections using both the modified KDS 14 model and existing design codes. Overall, all prediction models exhibited similar trends. Further, they were consistent with the experimental results according to variations in design parameters, including concrete compressive strength, slab effective depth, FRP axial stiffness, and column dimension.
Systems of building construction. Including fireproof construction, concrete construction
Abstract Nano materials made from graphene have emerged as highly effective additives that can significantly improve the engineering properties of cement-based composites. This research investigated the impact of adding graphene nanoplatelets (GNP) on the performance of concrete, using both sonicated and non-sonicated GNP dispersions. The study evaluated fresh concrete properties through slump tests, and mechanical characteristics were analyzed at 7, 14, and 28 days by conducting tests for compressive, flexural, and tensile strength. Homogeneity was assessed using ultrasonic pulse velocity (UPV), while durability was evaluated by examining water absorption (Ai), apparent volume of permeable voids (AVPV), and sorptivity. Furthermore, scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS) were employed to study how GNP affected the microstructure of concrete. The research found that the most significant improvements in engineering properties occurred when sonicated GNP dispersions were added at a concentration of 0.25 wt%. This addition resulted in enhancements of compressive, flexural, and tensile strengths by 20.8%, 10.5%, and 11.4%, respectively, at 28 days. UPV also improved by 12.9% at the same GNP concentration. Furthermore, Ai, AVPV, initial sorptivity, and secondary sorptivity decreased by 28.3%, 26.3%, 22%, and 27%, respectively, at 28 days. Microscopic analysis indicated that GNP contributed to reinforcing the microstructure of concrete through nucleation and filling effects, thereby enhancing the material's overall engineering performance.
Systems of building construction. Including fireproof construction, concrete construction
Voronoi diagrams are essential geometrical structures with numerous applications, particularly astrophysics-driven finite volume methods. While serial algorithms for constructing these entities are well-established, parallel construction remains challenging. This is especially true in distributed memory systems, where each host manages only a subset of the input points. This process requires redistributing points across hosts and accurately computing the corresponding Voronoi cells. In this paper, we introduce a new distributed construction algorithm, which is implemented in our open-source C++ 3-dimensional Voronoi construction framework. Our approach leverages Delaunay triangulation as an intermediate step, which is then transformed into a Voronoi diagram. We introduce the algorithms we implemented for the precise construction and our load-balancing approach and compare the running time with other state-of-the-art frameworks. MadVoro is a versatile tool that can be applied in various scientific domains, such as mesh decomposition, computational physics, chemistry, and machine learning.
In this thesis, a new approach for constructing subdivision algorithms for generalized quadratic and cubic B-spline subdivision for subdivision surfaces and volumes is presented. First, a catalog of quality criteria for these subdivision algorithms is developed, serving as a guideline for the construction process. The construction begins by generating the desired subdominant eigenvectors as the vertices of regular convex 3-polytopes for volumes using circle packings. Subsequently, these polytopes are utilized to construct a Colin-de-Verdiere-matrix for the generalized quadratic and a Colin-de-Verdiere-like matrix for the generalized cubic B-spline subdivision. These matrices are then adjusted using the matrix exponential to obtain subdivision matrices with the desired properties. All subdivision algorithms introduced in this paper empirically exhibit a subdominant eigenvalue of 1/2 with the desired algebraic and geometric multiplicity. For the quadratic case, this property can even be formally proven. Moreover, the corresponding eigenvectors form a convex polytope in the central region for the generalized quadratic B-spline subdivision algorithms, while for the generalized cubic B-spline subdivision algorithms, they represent the refinement of a convex polytope. Additionally, the constructed subdivision algorithms fulfill various other quality criteria, such as affine invariance and convex hull preservation and respecting all symmetries. Furthermore, it is demonstrated that the original Catmull-Clark algorithm is not suitable for generalization to volumetric subdivision and that the established subdivision algorithms [Baj+02] and [JM99] do not exhibit a suitable spectrum for several combinatorial configurations. Additionally, research approaches for the volumetric case are proposed, aiming to generalize from hexahedral to arbitrary structures.
Argument structure constructions (ASCs) offer a theoretically grounded lens for analyzing second language (L2) proficiency, yet scalable and systematic tools for measuring their usage remain limited. This paper introduces the ASC analyzer, a publicly available Python package designed to address this gap. The analyzer automatically tags ASCs and computes 50 indices that capture diversity, proportion, frequency, and ASC-verb lemma association strength. To demonstrate its utility, we conduct both bivariate and multivariate analyses that examine the relationship between ASC-based indices and L2 writing scores.
Based on the Wronski determinant, we propose the construction of linearly independent orthogonal functions in any Hilbert function space. The method requires only an initial function from the space of the functions under consideration, that satisfies minimal properties. Two applications are considered, including solutions to ordinary differential equations and the construction of basis functions. We also present a conjecture that connects the latter two concepts, which leads to what we call the Wronski basis.
The demand for rapid, cost-effective, and sustainable infrastructure solutions has driven significant advancements in prefabricated concrete technology over the past decade. This study explores recent innovations in the design, production, and implementation of prefabricated concrete components within modern infrastructure projects, including bridges, buildings, and transportation systems. Emphasis is placed on modular construction techniques, high-performance materials, digital fabrication methods, and connection systems that enhance structural efficiency, durability, and construction speed. Case studies from urban infrastructure developments illustrate how prefabrication reduces construction time, minimizes on-site labor, improves quality control, and decreases environmental impact through material optimization and waste reduction. Furthermore, the integration of Building Information Modeling (BIM) and automation in precast fabrication facilities has streamlined the design-to-production workflow, enabling greater precision and customization. The paper concludes that prefabricated concrete technology plays a pivotal role in addressing the growing infrastructure needs of rapidly urbanizing societies while supporting the global transition toward more sustainable construction practices.
Maria Teresa Calcagni, Giovanni Salerno, G. Cosoli
et al.
The sustainable management of Construction and Demolition Wastes (CDWs) represents a crucial challenge for the European Union, considering that this wastes stream constitutes one of the main sources of man-made solid wastes. The implementation of strategies aimed at the recovery and recycling of these materials is essential to reduce the environmental impact of the construction sector and to foster the transition towards a circular economy model. However, one of the main obstacles for effective reuse and/or recycling of CDWs lies in the complexity of their composition, which includes a wide range of materials such as concrete, bricks, ceramics, metals, and wood, not rarely contaminated with harmful substances. In this context, this data article presents a comprehensive material library designed to collect, organise, and make available data from advanced material characterisation analyses based on vision systems data. Specifically, the library focuses on data obtained through two measurement techniques: infrared (IR) thermography and hyperspectral imaging (HSI). These methodologies were selected for their ability to provide complementary information on the chemical composition and physical properties of materials. The material library was developed as part of an in-depth study of CDW from building demolition and renovation operations in several EU countries. The data collection process included the preparation and analysis of representative samples, with the aim of ensuring maximum accuracy and reproducibility of the measurements. The data obtained were standardised and organised in a format compatible with the main statistical analysis and machine learning tools to facilitate their integration into predictive models and decision-making processes. The article describes in detail the library structure, data collection protocols, and practical applications in the fields of waste management and sustainable construction. In addition, the benefits of this resource for the scientific and industrial community are discussed, including the possibility of using the data to develop/fine-tune artificial intelligence (AI) algorithms capable of optimising sorting and recycling processes by recognition and discrimination among different types of CDW material using the aforementioned sensors. The material library represents a significant contribution to addressing the challenges posed by CDW management, promoting a more efficient use of resources and reducing the environmental impact of construction and demolition activities. This extensive database not only facilitates material characterisation and separation but also represents a solid basis for future technological innovation in the construction sector.
The introduction of artificial intelligence (AI) technologies, the construction industry is on track for a technological revolution. In order to investigate the potential of artificial intelligence (AI) to improve sustainability, safety, and efficiency in the construction industry, this research paper offers a thorough examination of these applications. The study looks at several AI methods, including robots, computer vision, machine learning, and natural language processing, and how they are used in the design, planning, scheduling, monitoring, and maintenance phases of the building lifespan. In order to show the concrete advantages of AI in maximizing resource allocation, cutting project delays, enhancing quality control, and minimizing risks, it also looks at case studies and real-world applications. The study also discusses ethical issues and addresses issues like security of data, workforce upskilling, and interaction with current systems. This report offers useful insights for practitioners, policymakers, and researchers interested in maximizing the revolutionary potential of artificial intelligence (AI) in the construction industry by integrating existing research and industry trends. In the construction industry, any error, miscalculation, or misinterpretation can result in claims, delays in projects, and large cost overruns. The documentation and construction contracting processes are very complex and time-consuming. This research is done to make the process of documentation easy using the AI tools. The respondent’s opinion is consistent (Cronbach alpha is greater than 0.80). Educational qualification is influencing application of AI in construction industry by stating that construction industry gets benefitted from AI-powered construction simulation tools helps in accurate 3D modelling for monitoring the progress of the project and also influencing the application of AI in construction industry through Workers are resistant to adopt AI technology due to their lack of skill & awareness in using this technology as a barrier/challenge and also influencing application of AI in construction industry by proving the phenomenal level of acceptance for Collaboration with AI technology developers helps in adopting the AI technologies in construction industry as an enabler to the challenges of application of AI in construction industry.
Modular construction has emerged as a promising alternative to traditional building methods in Portugal, driven by the need for faster, more sustainable, and economically viable solutions. This article critically analyzes the advantages, structural typologies, and technical challenges associated with this methodology. Through technical review and analysis of practical case studies, steel, precast concrete, CLT timber, and hybrid systems were evaluated based on criteria such as seismic resistance, sustainability, assembly time, and connection complexity. The results indicate that modular construction offers significant gains in efficiency, budget control, and environmental performance, especially when integrated with technologies such as BIM and factory automation. However, relevant obstacles remain, including cultural barriers, legislative gaps, logistical difficulties, and technical limitations specific to each structural typology. Cases such as the B&B Hotel in Guimarães demonstrate the potential of the modular approach, while projects like the one in Loures highlight the challenges of its implementation. The article concludes that, to consolidate modular construction in Portugal, it is essential to promote technical standardization, adapt financing models, and invest in structural innovation. The trend points to the growing use of steel and CLT in sustainable and seismic-resistant solutions, reinforcing the strategic role of modular construction in the future of Portuguese civil engineering.
This study investigates the utilization of phosphogypsum (PG), an industrial byproduct, as a sustainable additive in reinforced truss concrete slabs to promote eco-friendly construction practices. Through static loading tests (monotonic/cyclic) under mixed boundary conditions (simply supported fixed), four slabs—including 2% PG-modified and ordinary concrete—were evaluated for mechanical performance, stress strain response, deflection, and crack propagation. The results demonstrated that PG enhanced slabs achieved comparable strength to conventional counterparts while exhibiting superior structural integrity at failure, highlighting PG’s potential to reduce environmental waste without compromising performance. Finite element analysis (ABAQUS2023) closely aligned with experimental data (<5% error), validating the model’s reliability in predicting failure modes. The study underscores PG’s viability as a circular economy solution for green building materials, offering dual benefits of waste valorization and resource efficiency. These findings advance sustainable construction by providing actionable insights for integrating industrial byproducts into high-performance structural systems, aligning with global decarbonization goals.
Predicting concrete’s compressive strength (CS) is a crucial and challenging task in civil engineering as it directly impacts the longevity and structural integrity of infrastructure initiatives. Precise estimation of the water–cement ratio (W/C) is essential for guaranteeing the structural integrity of structures since it is a critical parameter that greatly affects concrete’s CS. This study carries out an extensive investigation of the prediction of the W/C of concrete, utilizing the enormous potential of machine learning, including the backpropagation neural network (BPNN), bilayer neural network, boosted tree algorithm, bagged tree algorithm (BGTA), and support vector regression (SVR), using 108 datasets. We integrate artificial intelligence models with traditional engineering techniques to develop a reliable, precise, and efficient forecasting system. The study input includes curing days (D), fiber (F), cement (C), fine and coarse aggregate (FA and CA), density (Den), CS, water (W), and W/C as the output variables. The result shows that, in comparison to the other models, BGTA-M3 achieved the best performance evaluation criterion. In the calibration and verification phases, NSE, PCC, R, and WI = 1 and MAPE = 0.00, respectively. BPNN-M3 had an MAPE of 0.0004 in the verification phase. The study uses SHapley Additive exPlanations (SHAP), an explainable artificial intelligence (AI) technique, to improve decision-making in complex systems, with cement “C” significantly contributing to higher predictions in SVR-M2. Future studies should expand the dataset to include information from diverse geographic areas, environmental conditions, and concrete mixes to enhance the applicability and dependability of the models.