Search Results (6,005)

Carbon-neutral strategies have become the focus of international attention, and many countries around the world have adopted building-integrated photovoltaic (BIPV) technologies to achieve low-carbon building operation by utilizing power-generating building materials to generate energy in buildings. The purpose of this study is to review the basic status of the development of building-integrated photovoltaic (BIPV) technologies in China, to identify and analyze the existing problems and challenges, and to propose optimization strategies and methods so as to better promote the overall development of green buildings and net-zero energy consumption buildings in China and the world. Primarily, the research area of BIPV is focused on the Chinese region through a case study approach. Subsequently, it elaborates on the theoretical basis of zero-net energy buildings and BIPV as well as the current status of the construction of the world’s low-carbon building standard system, and it summarizes the annual electricity generation of zero carbon buildings adopting BIPV in some European countries. Then, the article further quantitatively and comprehensively analyzes six successful BIPV application cases in China, and it graphically and visually evaluates and demonstrates the average annual net-zero energy performance percentage of the application cases by using the EPI evaluation and measurement tool. At the same time, based on the results of the above assessment, the challenges facing the development of BIPV in China are summarized, and specific incentives for new BIPVs are proposed to address the challenges as well as strategic approaches to optimize BIPVs that are applicable to China as well as Europe and the US. Ultimately, it is concluded that several classic BIPV building cases have achieved essentially 100% net-zero energy operation and maintenance with significant reductions in CO₂ emissions and savings of tens of thousands of tonnes of coal consumption. This shows that BIPV technology is gradually developing and maturing in China, and there are great advantages and incremental development space for promoting BIPV in China in the future. The application and promotion strategy of its results in China is also applicable to other countries in the world. It is hoped that based on this experience, countries around the world will implement the “carbon neutral” strategy and zero-net energy consumption development more efficiently and with higher quality so as to realize a greener and cleaner future. Full article

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors; the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model’s performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample problems with different dimensions, as well as a case study on residential apartment construction projects by a contracting company. In this respect, the answers obtained from solving the model using the multi-objective DE algorithm were better and superior to the NSGA-II algorithm and had a more favorable performance. Generally, the results indicate that using the integrated multi-objective mathematical model in the present research for managing and scheduling multi-mode resource-constrained multi-project problems, especially in the construction industry, can lead to an optimal state consistent with the desired objectives and can significantly improve the progress and completion of projects. Full article

Taking the main beams of historical buildings as the engineering background, existing theoretical research results related to influencing structural factors were used along with numerical simulation and data fusion methods to examine their integrity. Thus, the application of multifactor data fusion in the safety assessment of the main beams of historical buildings was performed. On the basis of existing structural safety assessment methods, neural networks and rough set theory were combined and applied to the safety assessment of the main beams of historical buildings. The bearing capacity of the main beams was divided into five levels according to the degree to which they met current requirements. The safety assessment database established by a Kohonen neural network was clustered. Thus, the specific evaluation indices corresponding to the five types of safety levels were presented. The rough neural network algorithm, integrating the rough set and neural network, was applied for data fusion with this database. The attribute reduction function of the rough set was used to reduce the input dimension of the neural network, which was trained, underwent a learning process, and then used for predictions. The trained neural network was applied for the safety assessment of the main beams of historical buildings, and six specific attribute index values corresponding to the main beams were directly input to obtain the current safety statuses of the buildings. Corresponding management suggestions were also provided. Full article

As urbanization continues to advance rapidly, the emergence of biophilic design offers a positive perspective to address the alienation between humans and nature, becoming a hot research topic in areas related to human living environments. Biophilic design, as a design concept inspired by nature, has positive significance in promoting the development of ecological diversity and improving human physical and mental health. This paper makes a comparative analysis of two of China’s residential communities in the same high-density environment through the main influencing factors of plot ratio, greening rate, external facades environments, and internal living space. Starting from the five senses of the human body, namely, sight, hearing, touch, smell, and taste, this paper aims to investigate the design of living spaces through the lens of biophilic design, and proposes a biophilic design model, along with strategies and recommendations tailored to high-density urban environments, in the hope of serving as a valuable reference and source of inspiration for future healthy dwelling design. Full article

Despite the excellent ductility and energy absorption properties of engineered cementitious composites (ECCs), a low modulus of elasticity and excessive drying shrinkage remain some of its major disadvantages. With the current trend in the application of nanotechnology in cementitious composites research, the effect of graphene oxide (GO) on the properties of ECCs is yet to be fully investigated, despite its promising results in ordinary cement paste, mortar, and concrete. ECCs need extensive material tailoring to provide the required mechanical characteristics and controlled fracture size with strain-hardening behavior. Striking a balance between these crucial hardened aspects of ECC without compromising any desired properties is a challenge. Hence, the main aim of the study reported in this paper is to use the response surface methodology (RSM) multi-objective optimization technique to identify an appropriate GO content via the weight of cement and also the volume fraction of polyvinyl alcohol (PVA) fiber as input variables that positively impact ECCs’ properties. Using RSM’s central composite design (CCD), 13 mixtures of various combinations of the variables (GO: 0.05%, 0.065%, 0.08%; PVA: 1%, 1.5%, 2%) were developed. Six responses were studied, including compressive strength, direct tensile strength, tensile capacity, flexural strength, modulus of elasticity, Poisson’s ratio, and drying shrinkage. Moreover, the microstructural properties of the composites were assessed using field-emission scanning electron microscopy (FESEM) and mercury intrusion porosimetry (MIP). The outcomes revealed that all the properties of ECCs were significantly enhanced by adding an optimum amount of 0.05% GO and 1–1.5% PVA fiber volume fractions. A maximum increase in 30%, 35%, 49%, and 33.9% in the compressive strength, direct tensile strength, flexural strength, and modulus of elasticity, respectively, of the mixes with 0.05% GO addition was recorded. It is demonstrated that the use of 0.05% GO as a nanoscale particle can provide good outputs for the construction industry. Full article

The construction industry is increasingly focused on sustainability, with a particular emphasis on reducing the environmental impact of cement production. One approach to this problem is to use recycled materials and explore eco-friendly raw materials, such as alumino-silicate by-products like fly ash, which can be used as raw materials for geopolymer concrete. To enhance the ductility, failure mode, and toughness of the geopolymer, researchers have added crumb rubber processed from scrap tires as partial replacement to fine aggregate of the geopolymer. Therefore, this study aims to develop rubberized geopolymer concrete (RGC) by partially replacing the fine aggregate with crumb rubber (CR). To optimize the mechanical properties of RGC, response surface methodology (RSM) has been used to develop 13 mixes with different levels and proportions of CR (10–30% partial replacement of fine aggregate by volume) and sodium hydroxide molarity (10–14 M) as input variables. The results showed that the strength properties increased as the molarity of NaOH increased, while the opposite trend was observed with CR. The maximum values for compressive strength, flexural strength, and uniaxial tensile strength were found to be 25 MPa, 3.1 MPa, and 0.41 MPa, respectively. Response surface models of the mechanical strengths, which were validated using ANOVA with high R² values of 72–99%, have been developed. It has been found that using 10% CR with 14 M sodium hydroxide resulting in the best mechanical properties for RGC, which was validated with experimental tests. The result of the multi-objective optimization indicated that the optimum addition level for NaOH is 14 M, and the fine aggregate replacement level with CR is 10% in order to achieve a rubberized geopolymer suitable for structural applications. Full article

Stability calculation is the main objective during the analysis of domes. To investigate the effects of the initial defect, geometric nonlinearity, and material nonlinearity on the stability performance of different dome structures, 60 m numerical models were built and optimized by an iterative force-finding APDL program. Then, linear buckling analysis, geometric nonlinear stability analysis, geometric nonlinear stability analysis with initial defects, and dual nonlinear analysis with initial defects were discussed to compare the stability performance of ridge-beam cable domes (RCDs), suspen-domes, and conventional cable domes via finite element analysis. The results show that the buckling loads all follow the order of initial defect + dual nonlinear analysis < initial defect + geometric nonlinear analysis < geometric nonlinear analysis < linear buckling. The addition of ridge beams improves the overall stability and transforms the instability modes from local concave instability to overall torsional buckling. The ultimate load amplification coefficients of the RCD are close to those of the suspen-dome, while the vertical displacements of the RCD are more than those of the conventional cable dome, so the RCD has sufficient stiffness to reduce local displacement. Under 2–3 load combinations, internal ridge beams change from a tensile-bending state to a compressive-bending state, causing the entire instability of the RCD afterwards. Full article

A low stiffness makes long-span suspension bridges sensitive to loads, and this sensitivity is particularly significant for wind-induced nonlinear vibrations. In the present paper, nonlinear vibrations of suspension bridges under the combined effects of static and vortex-induced loads are explored using the nonlinear partial differential–integral equation that models the plane bending motion of suspension bridges. First, we discretized the differential–integral equation through the Galerkin method to obtain the nonlinear ordinary differential equation that describes the vortex-induced vibrations of the bridges at the first-order symmetric bending mode. Then, the approximate analytical solution of the ordinary differential equation was obtained using the multiple scales method. Finally, the analytical solution was applied to reveal the relationships between the vibration amplitude and other parameters, such as the static wind load, the frequency of dynamic load, structural stiffness, and damping. The results show that the static wind load slightly impacts the bridge’s vibrations if its influence on the natural frequency of bridges is ignored. However, the bridge’s vibrations are sensitive to the load frequency, structural stiffness, and damping. The vibration amplitude, as a result, may dramatically increase if the three parameters decrease. Full article

The paper focuses on the analysis of the values of the static modulus of deformation obtained by the application of the test procedure specified in the methodology for the diagnostics of the sub-ballast layers used for German railways (DIN 18 134) and the Railways of the Slovak Republic (Regulation TS4). The purpose of the study was to determine the correlation between the measured values of the static modulus of deformation according to the above-mentioned methodologies based on a series of experimental measurements on an experimental field built at a scale of 1:1. It also aimed to develop a numerical model characterising the behaviour of the loaded environment during the experimental measurements using the finite element method, which can subsequently be used for the design of the structural composition of the sub-ballast layers. For the purpose of the experimental measurements, a sub-ballast layer of 0/31.5 mm crushed aggregate of different design thicknesses was applied to the sub-ballast layers. A polynomial dependence with a high value of the reliability coefficient can be found between the results of the static modulus of deformation obtained using the mentioned measurement methodologies during the quality inspection of the implemented construction works. This dependence is valid for the specific boundary conditions of the experimental measurements performed (subsoil of clay with gravel admixture and the sub-ballast crushed aggregate layer of 0/31.5 mm dolomitic gravel). In the future, establishing correlation dependencies for other boundary conditions and structural material compositions can be considered. Full article

Nature is the major source and basis for architectural design. It is beyond human ability to create the same unlimited changes and dimensions. One of the key actors in minimizing negative impacts on nature and the environment is the architect. Due to the different uses of nature in architectural design and the interdisciplinarity between the approaches and aspects of nature, this study aimed to explore the contributions of nature-based strategies to the architectural design domain and identify the comprehensive relationship between nature and architecture. Through using logical argument, the nature-based strategies of architectural design were classified according to four categories of architectural design principles in a predicted model. For testing and validating the model, one strategy, which included nine nature-based approaches of architectural design with 23 aspects, was evaluated, and the scopes of the approaches were identified. VOSviewer was used for data analysis, and the survey questionnaire method was used for the focus group of architects to evaluate Erbil City’s architectural design. In this survey, 328 responses were received, which were ranked using the four-point Likert scale (most of the time, some of the time, seldom, and never), and the t-test from SPSS software was used to compare the approaches of the selected nature-based strategy. The passive design was the only approach with a positive value from the t-test (3.805) with a p-value of 0.000. Among these 23 evaluated aspects, natural ventilation received the highest mean value (1.91). Full article

In order to predict the settlement of self-compacting solidified soil in foundation pit backfilling, finite element software is used to study the influence of soil properties and the surrounding structural properties of the foundation pit on the settlement of backfilled self-compacting solidified soil based on a foundation pit project in the city of Nanjing. The degree of influence of various factors influencing settlement is considered, a grey relational grade analysis is conducted, and input layer parameters of the neural network are determined based on the results of the grey relational grade analysis. Based on the GA-BP neural network model, the settlement of soil is predicted using numerical simulation results. The results reveal that the settlement and structural disturbance of self-compacting solidified soil after backfilling are smaller than those of fine silty sand; self-compacting solidified soil significantly improves the engineering performance of excavated soil. In the grey relational grade analysis, the six influencing factors that have high correlation with soil settlement can be used as input layer parameters for the neural network model. Among them, the correlation degree between elastic modulus and soil settlement is the highest, reaching 0.8402. The correlation degrees of the remaining five influencing factors are above 0.5, and the values are close. The GA-BP neural network can improve the overfitting situation of a BP neural network trapped in local optima, with R² reaching 0.9999 and RMSE only 0.0018 mm, achieving high-precision prediction of settlement of self-compacting solidified soil. Full article

Bridge vortex-induced vibration (VIV) refers to the vertical resonance phenomenon that occurs in a bridge when pulsating wind passes over it and causes vortices to detach. In recent years, VIV events have been observed in numerous long-span bridges, leading to fatigue damage to the bridge structure and posing risks to driving safety. The advancement of technologies such as structural health monitoring (SHM), machine learning, and big data has opened up new research avenues for the intelligent identification of VIV in bridges. Machine learning algorithms can accurately identify the VIV events from historical data accumulated by SHM systems, thus providing an effective method for VIV recognition. Nevertheless, the existing identification methods have limitations, particularly in their applicability to bridges lacking historical VIV data. This study introduces an adaptive VIV recognition method in the main girders of long-span suspension bridges based on Transfer Component Analysis (TCA). The method can accurately identify VIV patterns in real-time or in historical data, even when specific VIV data are not available for the target bridge. The proposed method exhibits suitability for multiple long-span bridges. Experimental validation is performed using the SHM datasets from two long-span suspension bridges. The results show that the proposed VIV identification method can recognize more VIV samples compared to the benchmark model. When using sensor 1 data of bridge B as the source domain to identify the VIV of the L-section of bridge A, the F1 score of the TCA-based method is 0.836, while the F1 score of the benchmark model is 0.165. In the other 11 cases, the F1 score of the proposed model is higher than 0.8, which demonstrates the method’s robust generalization capabilities. Full article

Precast prestressed concrete hollow-core slabs (HCUs) are structural elements with less self-weight, providing improved structural effectiveness in withstanding the straining action and allowing for a long span. This study investigated the additional strand slips and developed machine learning (ML) models for evaluating the final strand slips (Śf) of the precast HCUs. Two groups of HCUs, with nominal widths of 1.2 m and 0.55 m, were subjected to flexural loading conditions. One sample from each group was selected to form composite specimens by casting a concrete topping slab, and the restrain mechanism was attached at the ends of the additional HCU specimens. The experimental datasets used to train the ML models, including the support vector machine (SVM), multi-linear regression (MLR), and improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) models for the prediction of Śf. The efficacy of the IEPANN model compared to the nonlinear predictive models was evaluated, and the performances of the developed ML models were checked using the evaluation matrices. The results indicated that the prestressing strands with relatively higher initial strand slips may result in larger additional slips during flexural loading. The restraining mechanism and cast-in-place topping slab influenced the additional strand slip rate. The hybridized IEPANN model outperformed other classical models in estimating the additional slips with the R² values greater than 0.9 in the two modelling stages, indicating the efficacy of the IEPANN compared to the nonlinear predictive modes. Full article

Additive Manufacturing (AM) of the Ti–6Al–4V alloy has gained significant importance across various industries, including biomedical, aerospace, cellular, and land vehicle applications, due to its numerous benefits. The certification of performance and reliability of AM materials, particularly for critical applications, heavily relies on evaluating fatigue strength. In this study, a numerical analysis based on the finite element method is presented to predict the High Cycle Fatigue (HCF) behavior of AM Ti–6Al–4V alloy. The investigation focuses on exploring the sensitivity of material fatigue life to surface roughness and Ultimate Tensile Strength (UTS). Uniaxial tensile and High Cycle Fatigue (HCF) tests were conducted on Ti–6Al–4V alloy samples extracted from rectangular walls manufactured using the Laser Metal Deposition (LMD) process. The walls were surface machined prior to sample extraction. Porosity and surface roughness measurements were performed on the samples. Numerical simulations of the HCF tests were carried out, considering various surface roughness ranges and UTS values. The numerical results were then compared to experimental data. The findings consistently demonstrated that higher surface roughness led to a shorter fatigue life, while higher UTS values resulted in a longer fatigue life. The numerical solutions aligned with the experimental results, indicating the efficacy of the finite element method in predicting the fatigue behavior of AM Ti–6Al–4V alloy. These insights contribute to a better understanding of the relationship between surface roughness, UTS, and fatigue life of Ti–6Al–4V alloys manufactured by AM. Full article

Unfortunately, most of the previous work studying the fracture toughness of fibrous composites has deliberately ignored bridging the fiber onto the pre-crack/notch surfaces by creating such a crack as a through-thickness crack (TTC). Furthermore, no standard specifications for measuring the fracture toughness of fibrous composites have considered the fiber bridging through the pre-notch. Only a few pieces of research, no more than fingers on one hand, have addressed this problem by creating an actual crack, i.e., a matrix crack (MC) instead of a TTC. The challenge these researchers face is the inability to calculate the fracture toughness directly through the stress intensity factor (SIF) relationship because there is no geometry correction factor equation, f(a/d), for an MC. The main objective of the present work is to calculate f(a/d) and ascertain a relationship between the SIF and crack mouth opening displacement (CMOD) for an MC numerically using 3-D finite element analysis. An experimental program was also conducted to measure the fracture toughness of three types of concrete beams: high-strength concrete (HSC) beams with a TTC, HSC beams with an MC, and fiber-reinforced concrete (FRC) beams with an MC. The results showed that FRC beams with an MC have the highest fracture toughness and, subsequently, the highest resistance to crack growth. The numerical results revealed a suggested relationship between the SIF and CMOD of FRC beams with an MC. This relation was used to predict the fracture toughness of FRC with an MC by the critical value of CMOD measured experimentally. Full article