Structured Design Methodology Research Articles

Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete

Background Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually. Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis and design methodologies. These methods are also difficult to execute in practice. To reduce time and effort, ML might be a viable option. Material and Methods A set of keywords are designed to perform the search PubMed search engine with filters to not search the studies below the year 2015. Furthermore, using PRISMA guidelines, studies were selected and after screening, a total of 42 studies were summarized. The PRISMA guidelines provide a structured framework to ensure transparency, accuracy, and completeness in reporting the methods and results of systematic reviews and meta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest parts of original research include knowledge mapping, co-citation, and co-occurrence. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influential authors were in terms of both output and citations and which articles garnered the most citations overall. Conclusion ML has become a viable prediction method for a wide variety of structural industrial applications, and hence it may serve as a potential successor for routinely used empirical model in the design of concrete structures. The non-ML structural engineering community may use this overview of ML methods, fundamental principles, access codes, ML libraries, and gathered datasets to construct their own ML models for useful uses. Structural engineering practitioners and researchers may benefit from this article’s incorporation of concrete ML studies as well as structural engineering datasets. The construction industry stands to benefit from the use of machine learning in terms of cost savings, time savings, and labor intensity. The statistical and graphical representation of contributing authors and participants in this work might facilitate future collaborations and the sharing of novel ideas and approaches among researchers and industry professionals. The limitation of this systematic review is that it is only PubMed based which means it includes studies included in the PubMed database.

Innovative approach to estimate structural damage using linear regression and K-nearest neighbors machine learning algorithms

Conventional structural design methodologies often utilize elastic analysis techniques, such as the equivalent static force method and the response spectrum method. While these methods are known for their simplicity and computational efficiency, they prove inadequate in capturing the extent of structural damage caused by seismic forces. Additionally, employing nonlinear dynamic analysis to estimate structural damage represents a challenging and intricate task, posing difficulties for many structural designers. Consequently, the objective of this paper is to present an innovative methodology for evaluating seismic structural damage of moment-resisting frame structures. This involves the utilization of machine learning algorithms, which have been trained and tested on a large data set generated using a newly developed and numerically efficient simulation procedure. The machine learning algorithms employ both linear regression and K-nearest neighbors approaches to accurately replicate the Park-Ang structural damage index.

A Study on Automated Hospital Management System in Nigeria

This thesis underscores the critical importance of effective data management in the operational and survival aspects of contemporary hospitals. Acknowledging the imperative for continuous enhancements in this domain, the study recommends a proactive approach to explore and advance the existing hospital data management project by transitioning it to an online platform. This strategic move includes the implementation of robust security measures, particularly a secure access code system, to fortify the protection of sensitive information. The proposed transition aims not only to modernize the hospital's data management capabilities but also to establish a secure online infrastructure, guarding against unauthorized access and ensuring the integrity and confidentiality of critical healthcare data. The thesis concludes with a comprehensive overview of the development process for the computerized system. A detailed survey of the existing system laid the groundwork for the meticulous design of a database application focused on facilitating the search and retrieval of patient folders. The anticipated automated hospital management system is poised to significantly enhance the overall efficiency of healthcare services for both nurses and doctors, introducing notable improvements such as reduced time spent on locating patient folders, increased accuracy, and timely record preparation. Throughout the implementation phase, substantial efforts were dedicated to eliminating data redundancy and inconsistency, with a specific emphasis on enhancing the integrity of the stored data. The chosen software model, the waterfall model, provided a systematic and sequential approach to software development. Additionally, a structured design methodology was employed during the design stage to ensure clarity and efficiency. Recognizing the sensitivity of healthcare data, the thesis rigorously addresses security concerns by incorporating robust measures into the computerized system. These measures are designed to prevent unauthorized access, safeguarding the confidentiality and privacy of stored data. This multi-faceted approach to system development underscores the commitment to delivering a robust, efficient, and secure automated hospital management system aligned with the evolving needs of healthcare professionals, guaranteeing top-tier levels of data integrity and security.

Design and Development of a Secure Website for Asaba Real Estate Industry

This research work borders on development of a secure functional website for Asaba real estate industry where potential buyers can visit and make their choice. Survey carried out using web research, study of documents and oral interview showed that all is not well with the way and manner real estate transaction is carried out in Asaba. The main objectives of this research work are to develop interactive website that can bring estate owners and customers together in real time without the stress of travelling to locate the property, thus saving time and cost and provide real time information on availability of property through proper advertisement. The methodology adopted for this research is Structural Systems Analysis and Design Methodology (SSADM). The system was also implemented using Object Oriented PHP and MYSQL. The new system developed brought effectiveness and efficiency to the process of selling, buying and renting houses in Asaba. This further increased transparency, accountability, customer satisfaction and effective data collection from Asaba real estate industry. Keywords: Website, Real Estate, Transparency, Methodology and Encryption.

Development of a new acoustic prediction tool by integration of life cycle assessment

Recently, environmental awareness has been the key driver for using renewable materials that have low environmental impact and fulfill constructional requirements, such as timber. Despite the advantages of wood as a building material, it has a lower subjective quality of sound insulation. To fulfill the sound insulation requirements, it is, therefore, unavoidable to complement based-wooden assemblies with additional element(s). However, identifying the acoustic performance is costly and time-consuming. Therefore, developing an accurate prediction tool is vital. Since wood-based structures have been developed to consider the environmental aspects, the environmental performance of buildings should be integrated into the acoustic design. This paper aims to develop an acoustic design methodology for wooden structures using artificial neural network approach by integration of life cycle assessment (LCA). Various Lab-based measurements are used to develop the acoustic prediction tool. Then, a LCA study is conducted on the test assemblies. This paper initially found that wooded assemblies generally increase the environmental impacts to achieve better acoustic insulation. Moreover, different assemblies can meet the sound insulation requirements. Therefore, designers should cognize of environmental and acoustic trade-off by selecting assemblies that consider both aspects.

Structural design and material comparison for aircraft wing box beam panel

This paper aims to present a comprehensive investigation to obtain the structural calculations needed to design a rigid panel of aluminum alloy for the wing box beam of an ATR 72–500 aircraft. For this design process, several types of materials, including composites like CFRP, are considered so it is possible to compare the actual existing part made of aluminum to them, thus checking the advantages these new materials offer. The research presents an introduction to structural design and provides a study of the relevant literature. The aircraft's principal characteristics and performance abilities were collected so that structural loads can be computed. Research used several methods, a design using conventional methods, applying the theory of elasticity is performed using the Theory of Farrar, allowing us to obtain an analytical solution to the problem, followed by checking the obtained results using Ansys FEM software combined with the parts being designed with CATIA. Furthermore, this same panel is calculated using composite materials instead of conventional aluminum, allowing us to compare both solutions. This research shed light on the intricate process of aircraft structural design, materials selection, and calculation methodologies, highlighting the ongoing pursuit of new and advanced materials. This paper makes clear that using composite materials presents several advantages over traditional ones, allowing for lighter, safer, more fuel-efficient, and more sustainable aircraft. The use of composite materials in the construction of airplane structures is driven by many factors. The results show that the chosen composite materials reduce weight, are durable, have low maintenance requirements, reduce noise, enhance fuel economy, and are resistant to corrosion.

The engineering legacy of the FIFA World Cup Qatar 2022TM: the gahfiya-shaped Al Thumama Stadium

The structures of the Al Thumama Stadium could be categorised into four main building components: The permanent bowl structure with the lower and middle tiers are mainly cast-in-place reinforced concrete with precast seating units. Structural steel is utilised for the upper tier stands which is to be dismantled for legacy conversion after FIFA World Cup Qatar 2022™. The roof and façade system utilises steel and cable systems with a unique membrane cladding design derived from the cultural gahfiya symbol. The roof is a self-anchored tension-compression ring system based on the principle of a horizontal spoked wheel with one compression ring along the outer perimeter and two tension rings along the oculus. The façade consists of lightweight double-layered and pre-stressed cable nets spanning between curved vertical beams. A translucent PTFE coated fibreglass membrane with highly transparent mesh inserts form the cladding of the stadium and provide its unique architectural design. This article gives an overview of the entire building structure design and construction methodology.

Preferences of interaction sites for proton-induced photophysical signalling with a derivatized rhodamine probe

Chemosensors for selective and sensitive detection of analytes were developed following various structural, functional and operational design methodologies. The interaction between probe and analyte has been vital in chemosensor-based detection technologies. A pertinent question in such a system is to be convincingly addressed, that if the probes incorporating spatially-positioned multiple interaction sites offer preferences towards analyte binding for a favourable and optimal interaction. The derivatized Rhodamine based probe 1 exhibiting proton-induced dual mode signalling here contained several protonation sites. The absorption and fluorescence signal enhancement on protonation of the probe attributed to its proton-mediated spiro-ring opening. Out of several plausible conformations of protonated probe [1.H+], their DFT calculations revealed the proton binding preferentially to the N-donor atom on spiro-ring of 1 instead at other probable sites of protonation such as other hetero- donor atoms on the xanthene core or the carbonyl O- atom on the spiro-ring as the energetically favourable conformation. It was verified with the protonated probe complex's X-ray diffracted single crystal structure.

Fuzzy Adaptive Knowledge-Based Inference Neural Networks: Design and Analysis.

A novel fuzzy adaptive knowledge-based inference neural network (FAKINN) is proposed in this study. Conventional fuzzy cluster-based neural networks (FCBNNs) suffer from the challenge of a direct extraction of fuzzy rules that can capture and represent the interclass heterogeneity and intraclass homogeneity when the data possess complex structures. Moreover, the capability of the cluster-based rule generator in FCBNNs may decrease with the increase of data dimensionality. These drawbacks impede the generation of desired fuzzy rules, and affect the inference results depending on the fuzzy rules, thereby limiting their generalization ability. To address these drawbacks, an adaptive knowledge generator (AKG), consisting of the observation paradigm (OP) and clustering strategy (CS), is effectively designed to improve the generalization ability in FAKINN. The OP distills the characteristic information (CI) from data to highlight the homogeneity and heterogeneity of objects, and the CS, viz., the weighted condition-driven fuzzy clustering method (WCFCM), is proposed to summarize the CI to construct fuzzy rules. Moreover, the feedback between the OP and CS can control the dimensionality of CI, which endows FAKINN with the potential to tackle high-dimensional data. The main originality of the study focuses on the AKG and WCFCM that are proposed to develop the structural design methodology of FNNs. The performance of FAKINN is evaluated on various benchmarks with 27 comparative methods, and two real-world problems are adopted to validate its effectiveness. Experimental results show that FAKINN outperforms the comparison methods.

Refractive Fresnel liquid crystal lenses driven by two voltages.

We propose and demonstrate a high-performance refractive Fresnel liquid crystal (LC) lens with a simple electrode design. The interconnected circular electrodes enable the creation of a parabolic voltage distribution within each Fresnel zone using only two driving voltages. By controlling these voltages within the linear response region of LC material, the desired parabolic phase profile can be achieved. We provide a detailed discussion on the electrode structure design methodology and operating principles of the lens. In our experiments, we constructed a four-zone Fresnel LC lens with a total aperture of 8 mm. The results show that the optical power of the lens can be continuously adjusted from -1.30 D to +1.33 D. Throughout the process of electrically tuning the optical power, the phase distribution within each Fresnel zone maintains a parabolic profile. These results demonstrate the high-performance of the proposed Fresnel LC lens.

Design and Implementation of a Software Result Processing and Transcript Generation

Result processing can be seen as a continuous process of converting data (scores, grade points, credit units etc.) into a definite and meaningful information (statement of result, transcripts etc.). These results are used to check the performance of each student in various courses. These results when processed manually leads to many problems such as error during computation, insecurity of results, untidy results after changes must have been affected and work load on the exam officer(s) etc. However, designing and implementing, as well as the computerization of result processing and transcript generation system will reduce these problems to the barest minimum. Providing password can do this, which grants access to only authorized user(s). Corrections or changes are affected without making the work untidy. Also stress on exam officer(s) is or are reduced. The design and implementation will be developed using the structured system analysis and design methodology (SSADM). The new system was designed using Visual Basic and MS Access. This language was chosen because of its easy syntax and features for developing web applications.

Breaking symmetry restriction of chirality through spin-decoupled phase modulation utilizing non-mirror-symmetric meta-atoms.

The geometric phase in metasurfaces follows a symmetry restriction of chirality, which dictates that the phases of two orthogonal circularly polarized waves are identical but have opposite signs. In this study, we propose a general mechanism to disrupt this symmetric restriction on the chirality of orthogonal circular polarizations by introducing mirror-symmetry-breaking meta-atoms. This mechanism introduces a new degree of freedom in spin-decoupled phase modulation without necessitating the rotation of the meta-atom. To demonstrate the feasibility of this concept, we design what we believe is a novel meta-atom with a QR-code structure and successfully showcase circular-polarization multiplexing metasurface holography. Our investigation offers what we believe to be a novel understanding of the chirality in geometric phase within the realm of nanophotonics. Moreover, it paves the way for the development of what we believe will be novel design methodologies for electromagnetic structures, enabling applications in arbitrary wavefront engineering.

Модели и методы онтологического анализа данных в задаче структурного анализа и синтеза технических решений

The article explores the potential and demonstrates the effectiveness of using some models and methods of ontological data analysis in the problem of structural analysis and synthesis of technical decisions. Morphological analysis and synthesis of layout diagrams of artifacts are considered as a working methodology for structural design. A set-theoretic model of the morphological decision space is constructed for the classical version of the methodology. The model in-cludes the functional subsystems of the designed artifact, implementation options for these subsystems, and potential technical decisions themselves. Moreover, in contrast to existing versions of such formalization, the emphasis is on the description of binary relationships between the entities of the knowledge domain of morphological analysis. It is pro-posed to describe the incompatibility of implementation options for a single functional subsystem of a designed arti-fact and the restricted compatibility of implementation options for various functional subsystems within the frame-work of a single model, known in ontological engineering and, in particular in ontological data analysis as “existence constraints”. Analysis of one of the regularities inherent in this model made it possible to strictly substantiate the ap-proach common in structural design to describing the restricted compatibility of implementation options for various functional subsystems in the form of prohibitions. The advantages of using existence constraints to describe the mor-phological decision space are shown, and the frame of a formal ontology for the knowledge domain of morphological analysis is constructed. The structural synthesis of a technical decision is compared with the method of semantic identi-fication of an object, developed within the framework of ontological data analysis for processing incomplete and in-consistent empirical information about the results of measuring object properties. A conclusion is made about the suit-ability of the latter for solving the problem of structural synthesis when correcting the concept of a “normal” subset of properties and information about the costs and/or benefits of including each individual implementation option for the functional subsystems of the designed artifact.

Application of Graphic Statics and Strut-and-Tie Models Optimization Algorithm in Innovative Timber Structure Design

Timber has long been extensively employed within the construction industry as a famous, environmentally friendly, and low-carbon material. Considering that construction constitutes one of the most significant contributors to carbon emissions throughout the entire life-cycle of a building, there is an urgent desire to incorporate timber into this domain. Nevertheless, the use of timber faces inherent challenges stemming from its anisotropic nature, a result of the natural growth of timber fibers, which makes it challenging for it to function as a primary load-bearing material in coping with the various complex stresses inherent in architectural applications. Numerous designers have attempted to address this limitation through over-sized members and reinforcement at joints; however, none have satisfactorily resolved this issue in an economical manner. In this article, we introduce the Strut-and-Tie models (STM) from Graphic Statics (GS) and a topological optimization algorithm. This algorithm has the capability to generate a ‘load-minimizing path’ STM based on external load support conditions and the maximum structural path span. Regardless of the complexity of the initial external loads, each load transfer path in the optimized STM bears loads in only one direction, representing an optimal solution with minimal internal loads that align seamlessly with the characteristics of timber. Consequently, we endeavor to adopt this optimization algorithm to propose a structural design methodology, with the aspiration of designing structural systems that harness the unique attributes of timber perfectly and applying them to various architectural scenarios. Ultimately, we conclude that structural systems designed based on optimized STM are adaptable to diverse architectural contexts, and when applied to small-scale buildings, this method can save approximately 20% of material consumption compared to conventional timber frame structures, while in the case of mid-rise to high-rise buildings, it can lead to a material savings of approximately 5%.

Loss and reliability analysis of various solid-state battery reconfiguration topologies

The research domain about the selection and design methodology of battery topology structures for energy storage systems, grounded in practical application scenarios, remains significantly underexplored. Furthermore, a substantial gap exists in the current state of research, where the majority of studies lack a comprehensive analysis of losses and reliability associated with reconfigurable battery topology structures. This paper quantitatively analyzes existing MOSFET-based topologies from three key dimensions: losses, costs, and reliability. The study aims to discern the impact of different topology structures and energy storage systems with redundant units on these three dimensions. Subsequently, while ensuring the adaptability of the topology structure, we propose a novel reconfigurable battery system topology suitable for DC microgrids, accompanied by its corresponding control strategy. Through comparative analysis with three typical topology structures, this topology structure has been validated to exhibit certain advantages in terms of losses, reliability, and costs. Lastly, the feasibility of the introduced topology structure is demonstrated through simulation using MATLAB/Simulink. Simulation results indicate that the proposed topology structure not only provides precise control of charge and discharge currents but also demonstrates excellent battery balancing capabilities.

Multidisciplinary Design Analysis and Optimisation of Floating Offshore Wind Turbine Support Structures - Coupled Model Solution Strategies

Floating Offshore Wind Turbines (FOWT) can be installed at the sites of the most abundant wind resource. However, the design uncertainties and risks must be reduced to make them economically competitive. The design and optimisation methodologies for FOWT support structures adopted up to date tend to follow a sequential analysis strategy. Since the FOWT system involves multiple distinct, highly coupled disciplines, its analysis and design are challenging. This paper presents an efficient implementation of a coupled model of dynamics in an optimisation process by applying a Multidisciplinary Design Analysis and Optimisation (MDAO) methodology. The coupling effects studied include the interdependence of the mean offset of the platform and the aerodynamic and mooring loads, as well as the velocity of the platform and the viscous damping. The trade-off between the solution accuracy and efficiency for the coupled and uncoupled models was quantified, and a range of iterative solvers were compared. The study showed that the coupling between the platform offset and the mooring and thrust loads has a significant influence on the values of the responses, converging at higher surge and pitch offsets, higher mooring loads, and at lower thrust. These non-conservative results demonstrated the criticality of the two-way coupling between the platform excursion and the mooring loads. Notably, the coupled solution was achieved at a relatively low increase in the total solution time (+16%), due to the high efficiency of Broyden’s method.

Risk‐based optimization of concentrically braced tall timber buildings: Derivative free optimization algorithm

AbstractMass timber materials are attractive alternatives for tall‐timber buildings (TBs), where the need for sustainability is apparent. Innovative structural systems and design methodologies are needed to fulfil performance requirements according to modern performance based approaches. This paper deals with the design and optimization of buckling restrained braces as earthquake protection system for tall‐TBs through risk‐based design procedure. This procedure controls the mean annual frequency of exceedance of several limit states evaluated through a SAC‐FEMA approach and using response spectrum linear analyses on linearized models for demand assessment. The features of the optimization procedure and the linearized models are shown through an application on a 20‐story mass‐TB located in a high seismic zone. The optimization is executed through a derivative‐free algorithm, the generalized pattern Search, adopting several solution strategies whose efficiency and effectiveness for this kind of applications are shown and discussed. Finally, the results are compared and validated through the execution of non‐linear analyses within a multiple stripe framework.

Rational design process for gas turbine exhaust to supercritical CO2 waste heat recovery heat exchanger using topology optimization

Advances in additive manufacturing (AM) technologies and topology optimization methodologies are enabling sophisticated novel designs for heat exchanger performance. These tools have been demonstrated for development of high-performance heat sinks considering local or component-level performance factors (e.g., heat transfer per volume). To leverage such capabilities in larger-scale energy systems, structured design methodologies are needed that consider system-level factors, such as production cost, cycle-level efficiency, and operational constraints. This study seeks to develop and assess a rational approach for designing thermo-economically optimal heat exchangers for such applications. The methodology is illustrated through development of the Primary Heat Exchanger (PHX) for a supercritical carbon dioxide (sCO2) power cycle recovering exhaust heat from a 6 MW-scale natural gas turbine. The proposed approach begins with a detailed thermodynamic cycle model, which is then extended to account for techno-economics. Next, an optimal PHX heat transfer capacity target is identified, and a high-level geometry is selected based on operating characteristics. This geometry is then divided into repeating 2D prismatic unit cells, for which topology optimization is applied to identify high-performance heat transfer geometries. A key aspect of this process is that the unit cell geometries are optimized using the total PHX mass as the objective function, which represents a surrogate for production cost. This leads to distinct designs compared with approaches that optimize local heat transfer and flow resistance factors. A second topology-optimized design is developed using a representative local thermal-fluid performance objective function and is found to require 1.6× the mass of the design generated with the system-level techno-economic objective for the same unit cell size. Conventional-type PHX designs with simple longitudinally finned tubes are developed for comparison, and are found to require total masses 1.5× or greater than the design obtained with the proposed process. Integrating this approach with detailed additive manufacturing costing models and experimentally validated fabrication constraints can yield a streamlined workflow for HX design for future energy systems.

Topology Optimization Design of Resonant Structures Based on Antiresonance Eigenfrequency Matching Informed by Harmonic Analysis

Abstract In this article, we present a design methodology for resonant structures exhibiting particular dynamic responses by combining an eigenfrequency matching approach and a harmonic analysis-informed eigenmode identification strategy. This systematic design methodology, based on topology optimization, introduces a novel computationally efficient approach for 3D dynamic problems requiring antiresonances at specific target frequencies subject to specific harmonic loads. The optimization’s objective function minimizes the error between target antiresonance frequencies and the actual structure’s antiresonance eigenfrequencies, while the harmonic analysis-informed identification strategy compares harmonic displacement responses against eigenvectors using a modal assurance criterion, therefore ensuring an accurate recognition and selection of appropriate antiresonance eigenmodes used during the optimization process. At the same time, this method effectively prevents well-known problems in topology optimization of eigenfrequencies such as localized eigenmodes in low-density regions, eigenmodes switching order, and repeated eigenfrequencies. Additionally, our proposed localized eigenmode identification approach completely removes the spurious eigenmodes from the optimization problem by analyzing the eigenvectors’ response in low-density regions compared to high-density regions. The topology optimization problem is formulated with a density-based parametrization and solved with a gradient-based sequential linear programming method, including material interpolation models and topological filters. Two case studies demonstrate that the proposed design methodology successfully generates antiresonances at the desired target frequency subject to different harmonic loads, design domain dimensions, mesh discretization, or material properties.

Integrative Structural Design of Nonstandard Building Systems: Bridging the Gap Between Research and Industry

The utilization in architecture of computational design and digital fabrication, coupled with the exploration of new material systems, brings the potential to break with conventional ways of building. However, these emerging nonstandard structures also demand new ways of designing and proving the structure’s integrity and safety. This paper aims to develop an integrative structural design methodology and workflow to design, optimize, and validate nonstandard building systems by combining a multiscale and digital-physical approach. The methods are showcased with coreless filament winding (CFW) structures, an additive manufacturing method representative of nonstandard building systems made possible by robotic fabrication. The results demonstrate the potential of this methodology to shorten the gap between research and industry, facilitating the realization of innovative structures.