Construction Method Research Articles

Development of sustainable, high strength slag based alkali activated pavement quality concrete using agro-industrial wastes: properties and life cycle analysis

ABSTRACT Alkali-activated binder-based composites offer a sustainable alternative to cement-based systems, giving a conservational approach. This research incorporated rice-husk ash (RHA) and waste foundry sand (WFS) into ground-granulated-blast-furnace slag (GGBS)-based alkali-activated high-end pavement-quality concretes, optimising with air-curing for expressways, freeways and runway applications. This study promotes sustainable construction by efficiently utilising agro-industrial wastes (i.e., RHA, GGBS and WFS) promoting a global initiative to decrease environmental impact and to adopt eco-friendly pavement construction methods. This study systematically substitutes the primary binder-GGBS with RHA at 5% intervals. The binder was alkali-activated using measured amounts of liquid sodium silicate and sodium hydroxide flakes prepared at an activator modulus (Ms) of 1.25 by maintaining the sodium oxide dosage of 4.0% with respect to the total binder. In particular, 15% RHA (with 85% GGBS) and 20% WFS (with 80% river sand) satisfy mechanical design standards for workability and strength. A comprehensive life cycle analysis using extensive literature support assesses the environmental impact and sustainability of developed pavement quality composites, showing over 70% less energy use than Portland cement-based pavement mixes. Therefore, employing RHA and WFS in air-cured alkali-activated composite manufacture shows that agro-industrial waste components may be used to make sustainable, cost-effective pavement construction. This study effectively advocates for a shift towards environmentally responsible pavement construction, contributing to a greener, more sustainable future in concrete pavement engineering.

Design and implementation of dynamic s-boxes based on non-degenerate discrete chaotic systems

In recent years, digital chaotic systems have received considerable attention in the field of secure communications. However, during the digitalization of the system, the original properties of the chaotic system may change, resulting in the degradation of the dynamics. To address this problem, this paper designs a novel simplicial non-degenerate discrete chaotic system based on the inverse hyperbolic tangent function, and selects a three-dimensional discrete system as the object of analysis. Through the research conducted, it is found that the chaotic system exhibits high Lyapunov exponents under certain conditions. Furthermore, the excellent randomness of the system has been further validated by NIST SP800–22 tests. At the same time, this paper also proposes a dynamic S-box construction method based on the chaotic sequence generated from the three-dimensional chaotic mapping. By performing a series of basic operations and permutation treatment, many dynamic S-boxes can be generated. This paper comprehensively analyses the performance of S-boxes from two perspectives: single S-boxes and multiple S-boxes. The analysis covers issues such as bijective property, nonlinearity, strict avalanche criterion, differential approximation probability and bit independence criteria. The results of the performance analysis show that the dynamically generated S-boxes have excellent cryptographic properties, making them suitable for the design and application of cryptographic algorithms.

Development of A Workshop on the Topic of the Bionically Inspired Lightweight Design Method for 3D Printed Components

Additive manufacturing methods, particularly 3D printing, are widely utilized in research and engineering for crafting lightweight yet durable materials capable of withstanding substantial forces. Leveraging insights from biomechanical structures offers a deeper understanding of reinforcement techniques and optimal design strategies to enhance strength. This paper focuses on the development of a Draisine design, historically significant in Karlsruhe, as part of the BWSplus project "Drais3D-Trinational" workshop. Through a comprehensive analysis of bionic influences on lightweight design and 3D printing parameters, this study aims to create an optimal design framework for the workshop. Utilizing Computer-Aided Design (CAD) software such as SolidWorks and Creo Parametric, along with Finite Element Analysis using ANSYS R2023 Workbench, deformation and stress analysis are conducted. Investigation into 3D printing parameters, including infill patterns, temperature, support systems, and orientation, seeks to identify optimal solutions considering factors like processing time, robustness, and filament wastage. The objective is to explore the biomechanical influence on construction methods, concept design, and parameter construction in preparation for the Drais3D-Trinational workshop in March 2023. The expected outcomes include the presentation of two main designs with varied parameters, alongside analyses such as Finite Element Analysis and optimization of 3D printing parameters, emphasizing the role of bionic structures in defining the optimal Draisine design.

Remaining Useful Life of the Rolling Bearings Prediction Method Based on Transfer Learning Integrated with CNN-GRU-MHA

To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average RMSE of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average RMSE of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability.

Construction Education through Virtual Reality: A Game-based Approach for Interior Architecture Students

This study investigates a novel method of instructing interior architecture students on building specifics pertaining to walls by utilizing the combination of virtual reality (VR) and game-based learning. The creation and application of a virtual reality game called "VR ConstructED," which aims to improve knowledge of wall construction methods, materials, and design consequences, is the study's focus. This project intends to improve students' understanding of wall-related construction elements and their applicability in real-world circumstances by utilizing the immersive and interactive properties of virtual reality technology in conjunction with gamification methods. Through a case study involving academic staff and students at Bahçeşehir University, the efficacy of this pedagogical strategy will be assessed. Using a mixed-methods approach, the study collects quantitative data from surveys and qualitative data from semi-structured interviews. These questionnaires will evaluate the academic performance, contentment, and VR game participation of the students. The main goal is to clarify the advantages and difficulties of using virtual VR to teach construction features, such walls, so that curriculum designers and teachers may make informed decisions. Additionally, by illustrating how gamification and the integration of VR technology may greatly improve student engagement and academic achievement, this study hopes to contribute to the larger area of design education. The results of this study are anticipated to add to the body of knowledge in architectural education and stimulate more research into specific VR applications for design pedagogy.

A study on the role of construction methods of an edge dislocation on final arrangements of atoms via MD simulations

A study on the role of construction methods of an edge dislocation on final arrangements of atoms via MD simulations

A Certificate-Less Distributed Key Management Scheme for Space Networks

The specificity and complexity of space networks render the traditional key management mechanism no longer applicable. The certificate-less-based distributed spatial network key management scheme proposed in this paper combines the characteristics of space networks, solving the problems regarding the difficulty of implementing centralized key management in space networks and the excessive overhead required for maintaining public key certificates by constructing a distributed key generation center and establishing strategies such as private key updates, master key component updates, and session key negotiation. This method also avoids the key escrow problem inherent in existing identity-based key management schemes. This scheme provides the DPKG construction method for space networks; designs the update strategy for the DPKG node’s master key sharing, providing a specific update algorithm; introduces the batch private key update mechanism; and uses the mapping function to evenly distribute the node’s update requests throughout the update time period, avoiding the problem of overly concentrated update requests. After analysis and simulation verification, it was proven that the scheme can meet the necessary security requirements, offering good stability and scalability.

Estimation of SAM for India: An Application for India’s Energy Transition Targets

The social accounting matrix (SAM) for India was historically constructed based on the input–output table (IOT). However, since 2011–2012, the Government of India has been publishing the supply–use table, instead of the IOT. While the erstwhile IOT, published by the Government of India, had the same number of products and industries, the supply–use table provides one ‘supply matrix’ and one ‘use matrix’, each of which is a rectangular table with 140 products and 66 industries (for 2018–2019). Converting the supply–use table to a square IOT and subsequently extending it to SAM require the utilisation of various data sources, numerous steps and adjustments. Despite the usefulness of both IO and SAM matrices in macroeconomic policy design, not much literature is available on these. This study aims to bridge the gap by constructing IOT and SAM for India from the supply–use table incorporating information from many other sources and describing the method of construction of the matrices. Our IO and SAM also focus on various energy sectors, including different sources of power generation, biomass and so on, and disaggregate energy-intensive sectors like cement or aluminium, considering the immense usefulness of the energy-extended macro-structure to the research of energy and environment policies. The study focuses on the construction of a 59 × 59 SAM for India, with the base year of 2021–2022 incorporating three factors of production and ten categories of households. As an application of the newly constructed SAM, we have analysed the employment implication of India’s Nationally Determined Contribution emission commitments. JEL Codes: E16, C67, D57

Frontiers in construction 3D printing: self-monitoring, multi-robot, drone-assisted processes

AbstractTo overcome productivity issues and revolutionize the stagnating construction industry, a large amount of research efforts has been devoted to robot-assisted construction technology. The advancements in robotics including mechanical system design, tool design, digital system design, and numerical control systems design enabled engineers to create complex geometries that are infeasible for conventional construction methods. In addition, innovative robotic systems that utilize mobile platforms, multiple robots, and unmanned aerial vehicles have demonstrated significant promise in fully automating the construction process. This work will provide a perspective on the state-of-the-art applications of robotics in the revolution of construction, where a comprehensive review of the current development of the relevant software and hardware, 3D concrete printing (3DCP), robot-assisted assembly of discrete prefabricated blocks, real-time quality monitoring and feedback control systems, and typical innovative robot-assisted structural designs are conducted. Finally, the limitations of existing robot-assisted construction technology are identified, which leads to several recommendations for future research toward fully automatic construction.

A novel label-aware global graph construction method and spiking-coded graph neural network for intelligent process fault diagnosis

Fault diagnosis plays a crucial role in ensuring the safety and efficiency of industrial processes. However, traditional techniques often face difficulties in handling large-scale data characterized by complex structures and relationships. To efficiently represent industrial data as graphs and develop a low-energy-cost feature extraction model, a novel label-aware global graph construction method and a spiking graph convolutional network (SGCN) are proposed in this study to achieve intelligent process fault diagnosis. The label-aware method enhances graph data representation by capturing intrinsic correlations and global features. The SGCN integrates graph convolutional layers with spiking encoding, enabling effective feature extraction while offering computational efficiency advantages. A weighted loss function is introduced to mitigate data imbalance issues. Experiments on the Tennessee Eastman process, the Three-phase Flow Facility, and the de-propanizer distillation process demonstrate SGCN’s superior performance over baseline models in various fault scenarios, while significantly reducing computational costs. The proposed method offers promising potential for reliable and efficient fault diagnosis in complex real-world industrial environments.

Virtual sound source construction based on adaptive crossfade processing with electro-dynamic and parametric loudspeaker arrays

Among the various high-presence sound field reproduction technologies, we focus on wave field synthesis (WFS) using a loudspeaker array, as it can construct a virtual sound source (VSS) with few restrictions. A VSS using parametric array loudspeakers (PALs) can provide more accurate direction perception than VSS using electro-dynamic loudspeakers (EDLs) because PALs hold sharp directivity by utilizing ultrasounds. However, the distance perception tends to be degraded. We previously proposed a VSS construction based on WFS with hybrid control of an EDL and a PAL arrays. This method enhances distance perception by exploiting the difference in radiation characteristics between PALs and EDLs. However, the difference in frequency responses between PAL and EDL is noticeable to the listener when switching between them. In this paper, to enable more smooth transition of the EDL and PAL arrays, we propose a VSS construction method based on adaptive crossfade processing with EDL and PAL arrays. For an arbitrary VSS construction position, the coefficients of crossfade for each frequency are determined by an adaptive filter based on measured impulse responses of VSSs with EDL and PAL arrays. We demonstrated the effectiveness of the proposed method through evaluation experiments.

A graph-based method for automatic graded diagnosis of depression using EEG signals

Depression is a common psychological disorder typically categorized into four grades according to the symptom severity. Accurately diagnosing different grades of depression is significant for taking appropriate treatment strategies to prevent the condition from worsening. Electroencephalography (EEG) has the potential to mirror the prevailing mental state of patients, providing a possible way to diagnose depression. However, it is still a challenge to extract EEG features that reflect varying degrees of depression for automatic grading diagnosis. It has been pointed out that depression involves the intensification of abnormal interactions among distinct brain regions as it advances, which suggests that brain spatial information may play a key role in tracking different grades of depression. Based on this, combined with deep learning models, this paper proposes a new graph-based method for automatic grading diagnosis of depression. A novel graph-based brain network (BN) construction method is first proposed to convert EEG into graph structure containing brain spatial information, where the individual-level (IBN) and group-level brain networks (GBN) are constructed respectively. Next, a new deep learning model P-I-GAT is built based on graph attention networks (GAT) for identifying EEG signals from depression patients with different severity. In specific, the “P” represents the prior information integration capability, synthesizing attribute information from the IBN and structural information from the GBN. The resulting synthesized brain network contains prior information from these two different scales, referred to as the prior brain network (PBN). “I” denotes the multi-rhythm interaction capability, which adaptively assigns learning weights to each rhythmic PBN to fully exploit the information embedded within the PBNs. Ultimately, the proposed method is validated on the open dataset MODMA, achieving an accuracy of 88.57% and a specificity of 91.97%.

Quality Assurance of Steel Slag Asphalt Mixtures for Sustainable Pavement Surface Courses

The present study investigates the use of electric arc furnace (EAF) steel slag, a by-product of the steel industry, in asphalt pavement surface courses instead of virgin aggregates (VAs). Therefore, a general performance evaluation of such mixtures compared to conventional mixtures is carried out through laboratory and in situ tests, while both mixtures are environmentally assessed using the life cycle assessment (LCA) tool. The results of the laboratory and in situ tests show that asphalt mixtures containing granulated EAF slag aggregates perform as well as mixtures containing only VA. In addition, the LCA results show that the use of EAF slag aggregates in the asphalt surface course has a lower environmental impact than the exclusive use of VA when it comes to the impact categories of acidification, climate change, marine and terrestrial eutrophication, energy consumption and photochemical pollution. In summary, these results show that replacing virgin aggregates with a proportion of EAF slag aggregate is a viable and sustainable method for road pavement construction.

Construction and Practice of Teaching Evaluation System Based on Data Mining

The teaching quality evaluation system based on data mining technology can accurately and fairly identify the core driving factors to improve teaching quality. This method adopts the analysis of big data correlation rules, including data collection and processing preparation steps, builds the data warehouse of association rules, and then generates an educational quality evaluation framework using the principle of data mining. Based on this, this paper analyzes the construction design and method of the teaching evaluation system under data mining, hoping to provide help for the improvement of the teaching evaluation system and the improvement of teaching quality.

CIRG-SL: Commonsense Inductive Relation Graph framework with Soft Labels for Empathetic Response Generation

Empathy, the ability to understand and respond to others’ emotions, is critical in dialogue systems, particularly for applications such as psychological counselling and casual conversation. However, existing approaches often struggle to accurately capture emotional transitions in user utterances and dialogue contexts across multiple levels of granularity. Additionally, the orthogonality of one-hot emotion representations overlooks the subtle relationships between emotion categories. To overcome these limitations, we propose a novel framework: the commonsense inductive relation graph with fine emotional soft labels for empathetic response generation. This framework refines the representation of user utterances by incorporating commonsense knowledge to derive external emotional cues, which are integrated into an adaptive inductive relation graph. This graph explicitly captures and models dynamic emotional trajectories throughout the dialogue. Additionally, we propose a fine-grained soft label construction method that more accurately reflects the nuanced distribution of emotions, replacing the rigid one-hot encoding with a continuous representation that better captures inter-emotion relationships. Our approach achieves strong performance on the benchmark EmpatheticDialogues dataset, with a perplexity score of 27.85, Dist-1 of 1.81, Dist-2 of 6.58, and a dialogue emotion accuracy of 55.65%. These results highlight the effectiveness of our framework in capturing subtle emotions and generating contextually rich, empathetic responses.

Primjena ekspertnih sustava u građevinarstvu

Expert systems represent “IF-THEN” algorithms that integrate human expert knowledge to solve complex problems. This area of research focuses on finding solutions to challenging situations that are often difficult to resolve using traditional methods. Expert systems have widespread applications in the field of construction. The paper presents some of these applications, including their use in building maintenance, inspection of water and sewage systems, geotechnical engineering, road maintenance, and the selection of appropriate construction materials. Artificial neural networks are effective in optimizing financial plans, estimating the duration of activities, assessing the productivity of work teams, and selecting contractors. Genetic algorithms find their application in resource allocation, selecting optimal solutions for task scheduling and project costs, as well as in building maintenance, while fuzzy logic aids in risk analysis, project management, and cost estimation. Expert methods can be integrated into information systems to provide consistent information necessary for the effective management of construction projects. A review of published papers on the application of expert methods in construction shows that these methods are already significantly represented across all sectors of the construction industry, but also indicates that there are still substantial opportunities for their future application.

Adaptive control of nonlinear time-varying systems with unknown parameters and model uncertainties

This paper investigates the adaptive control problem for nonlinear time-varying systems with unknown parameters and model uncertainties. A novel class of switching functions is designed, and its construction method is detailed, along with a proof of the continuity of its n−1 order derivatives. Two simple examples are provided to illustrate how the proposed congelation of variables method handles unknown high-frequency time-varying parameters in both the feedback and input paths. A new neural network control scheme is then developed, integrating an adaptive neural network controller with a robust controller. The smooth transition between these two controllers is ensured by the novel switching function, which guarantees global system stability. Furthermore, by combining the congelation of variables method with adaptive backstepping, a new adaptive tracking control scheme is proposed. This scheme effectively handles unknown high-frequency time-varying parameters while achieving asymptotic tracking of arbitrary reference signals. Simulation results show that the proposed novel adaptive control method delivers superior control accuracy while reducing energy consumption: it achieves an order of magnitude improvement over the traditional adaptive robust control method and two orders of magnitude improvement over the conventional sliding mode control method.

Unsupervised health indicator construction by a new Gaussian-student’s t-distribution mixture model and its application

The equipment’s remaining useful life (RUL) must be accurately estimated to guarantee its reliable operation. As a crucial part of data-driven RUL prediction, the health indicator (HI) construction method employing the distribution discrepancies can represent the variation trend of health conditions. However, the existing Gaussian mixture model based HI construction method cannot accurately estimate the long-tail distribution characteristics in some degradation data. Moreover, it cannot comprehensively mine the distribution characteristics of degradation data by leveraging different types of distributions. A novel Gaussian-student’s t-distribution mixture model (GSMM) that simultaneously considers Gaussian distribution and student’s t-distribution is developed in this work to estimate the distributions of normal and degradation data. Next, the distribution contact ratio metric (DCRM) is applied to measure the discrepancies between the baseline distribution of normal data and the distributions of test data at different moments. The bearing HI can be constructed with the acquired DCRMs. Finally, the effectiveness and merit of the developed HI construction approach are validated by two bearing life-cycle datasets. The experimental results illustrate that the GSMM-based HI performs better than other classical and state-of-the-art HIs. Additionally, the constructed HI is more suitable for bearing RUL prediction.

Research on Sound velocity model construction method in GNSS-A underwater positioning

Abstract GNSS-A technology is a submarine positioning technique used to establish ocean control networks, and its positioning results are primarily affected by errors in ocean sound velocity. To address this issue, this paper analyzes the spatial representativeness error of the weighted average sound velocity based on the GNSS-A positioning principle. It also explores the construction method of a sound velocity model in the time domain using the average sound velocity. A sinusoidal diurnal variation model for sound velocity is developed through expanded fitting. Finally, actual measured data is used to perform GNSS-A submarine positioning, verifying the effectiveness of the sound velocity model. The results show that when the horizontal distance of the sound ray is less than 1.7 times the water depth, the average sound velocity does not introduce significant systematic errors. The constructed sound velocity model aligns with local solar elevation patterns, improving the unit weight error of GNSS-A positioning results by several decimeters and controlling the mutual difference of positioning results from different tracks to the decimeter level.

Bi-colored expansions of geometric theories

This paper concerns the study of expansions of models of a geometric theory T by a color predicate p, within the framework of the Fraïssé-Hrushovski construction method. For each α∈(0,1], we define a pre-dimension function δα on the class of Bi-colored models of T∀ and consider the subclass Kα+ consisting of models with hereditary positive δα. We impose certain natural conditions on T that enable us to introduce a complete Π2-theory Tα for the rich models in Kα+. We show how the transfer of certain model-theoretic properties, such as NIP and strong-dependence, from T to Tα, depends on whether α is rational or irrational.