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The release of the large language model-based chatbot ChatGPT 3.5 in November 2022 has brought considerable attention to the subject of artificial intelligence, not only to the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment functionalities. In particular, ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. Accordingly, the research question arises to what extent writing of bachelor theses is still a valid learning and assessment format. Correspondingly, in this exploratory study, the first author was asked to write his bachelor’s thesis exploiting ChatGPT. For tracing the impact of ChatGPT methodically, an autoethnographic approach was used. First, all considerations on the potential use of ChatGPT were documented in logs, and second, all ChatGPT chats were logged. Both logs and chat histories were analyzed and are presented along with the recommendations for students regarding the use of ChatGPT suggested by a common framework. In conclusion, ChatGPT is beneficial for thesis writing during various activities, such as brainstorming, structuring, and text revision. However, there are limitations that arise, e.g., in referencing. Thus, ChatGPT requires continuous validation of the outcomes generated and thus fosters learning. Currently, ChatGPT is valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner’s meaningful engagement. Accordingly, writing a thesis is still a valid learning and assessment format. With further releases of ChatGPT, an increase in capabilities is to be expected, and the research question needs to be reevaluated from time to time.
As machine vision-based inspection methods in the field of Structural Health Monitoring (SHM) continue to advance, the need for integrating resulting inspection and maintenance data into a centralised building information model for structures notably grows. Consequently, the modelling of found damages based on those images in a streamlined automated manner becomes increasingly important, not just for saving time and money spent on updating the model to include the latest information gathered through each inspection, but also to easily visualise them, provide all stakeholders involved with a comprehensive digital representation containing all the necessary information to fully understand the structure’s current condition, keep track of any progressing deterioration, estimate the reduced load bearing capacity of the damaged element in the model or simulate the propagation of cracks to make well-informed decisions interactively and facilitate maintenance actions that optimally extend the service life of the structure. Though significant progress has been recently made in information modelling of damages, the current devised methods for the geometrical modelling approach are cumbersome and time consuming to implement in a full-scale model. For crack damages, an approach for a feasible automated image-based modelling is proposed utilising neural networks, classical computer vision and computational geometry techniques with the aim of creating valid shapes to be introduced into the information model, including related semantic properties and attributes from inspection data (e.g., width, depth, length, date, etc.). The creation of such models opens the door for further possible uses ranging from more accurate structural analysis possibilities to simulation of damage propagation in model elements, estimating deterioration rates and allows for better documentation, data sharing, and realistic visualisation of damages in a 3D model.
The current thesis presents research about new methods of citizen participation based on digital technologies. The focus on the research lies on decentralized methods of participation where citizens take the role of co-creators. The research project first conducted a review of the literature on citizen participation, its origins and the different paradigms that have emerged over the years. The literature review also looked at the influence of technologies on participation processes and the theoretical frameworks that have emerged to understand the introduction of technologies in the context of urban development. The literature review generated the conceptual basis for the further development of the thesis.
The research begins with a survey of technology enabled participation applications that examined the roles and structures emerging due to the introduction of technology. The results showed that cities use technology mostly to control and monitor urban infrastructure and are rather reluctant to give citizens the role of co-creators. Based on these findings, three case studies were developed. Digital tools for citizen participation were conceived and introduced for each case study. The adoption and reaction of the citizens were observed using three data collection methods.
The results of the case studies showed consistently that previous participation and engagement with informal citizen participation are a determinining factor in the potential adoption of digital tools for decentralized engagement. Based on these results, the case studies proposed methods and frameworks that can be used for the conception and introduction of technologies for decentralized citizen participation.
In ten chapters, this thesis presents information retrieval technology which is tailored to the research activities that arise in the context of corpus-based digital humanities projects.
The presentation is structured by a conceptual research process that is introduced in Chapter 1. The process distinguishes a set of five research activities: research question generation, corpus acquisition, research question modeling, corpus annotation, and result dissemination. Each of these research activities elicits different information retrieval tasks with special challenges, for which algorithmic approaches are presented after an introduction of the core information retrieval concepts in Chapter 2.
A vital concept in many of the presented approaches is the keyquery paradigm introduced in Chapter 3, which represents an operation that returns relevant search queries in response to a given set of input documents. Keyqueries are proposed in Chapter 4 for the recommendation of related work, and in Chapter 5 for improving access to aspects hidden in the long tail of search result lists.
With pseudo-descriptions, a document expansion approach is presented in Chapter 6. The approach improves the retrieval performance for corpora where only bibliographic meta-data is originally available. In Chapter 7, the keyquery paradigm is employed to generate dynamic taxonomies for corpora in an unsupervised fashion.
Chapter 8 turns to the exploration of annotated corpora, and presents scoped facets as a conceptual extension to faceted search systems, which is particularly useful in exploratory search settings. For the purpose of highlighting the major topical differences in a sequence of sub-corpora, an algorithm called topical sequence profiling is presented in Chapter 9.
The thesis concludes with two pilot studies regarding the visualization of (re)search results for the means of successful result dissemination: a metaphoric interpretation of the information nutrition label, as well as the philosophical bodies, which are 3D-printed search results.
Tropical coral reefs, one of the world’s oldest ecosystems which support some of the highest levels of biodiversity on the planet, are currently facing an unprecedented ecological crisis during this massive human-activity-induced period of extinction. Hence, tropical reefs symbolically stand for the destructive effects of human activities on nature [4], [5]. Artificial reefs are excellent examples of how architectural design can be combined with ecosystem regeneration [6], [7], [8]. However, to work at the interface between the artificial and the complex and temporal nature of natural systems presents a challenge, i.a. in respect to the B-rep modelling legacy of computational modelling.
The presented doctorate investigates strategies on how to apply digital practice to realise what is an essential bulwark to retain reefs in impossibly challenging times. Beyond the main question of integrating computational modelling and high precision monitoring strategies in artificial coral reef design, this doctorate explores techniques, methods, and linking frameworks to support future research and practice in ecology led design contexts.
Considering the many existing approaches for artificial coral reefs design, one finds they often fall short in precisely understanding the relationships between architectural and ecological aspects (e.g. how a surface design and material composition can foster coral larvae settlement, or structural three-dimensionality enhance biodiversity) and lack an integrated underwater (UW) monitoring process. Such a process is necessary in order to gather knowledge about the ecosystem and make it available for design, and to learn whether artificial structures contribute to reef regeneration or rather harm the coral reef ecosystem.
For the research, empirical experimental methods were applied: Algorithmic coral reef design, high precision UW monitoring, computational modelling and simulation, and validated through parallel real-world physical experimentation – two Artificial Reef Prototypes (ARPs) in Gili Trawangan, Indonesia (2012–today). Multiple discrete methods and sub techniques were developed in seventeen computational experiments and applied in a way in which many are cross valid and integrated in an overall framework that is offered as a significant contribution to the field. Other main contributions include the Ecosystem-aware design approach, Key Performance Indicators (KPIs) for coral reef design, algorithmic design and fabrication of Biorock cathodes, new high precision UW monitoring strategies, long-term real-world constructed experiments, new digital analysis methods and two new front-end web-based tools for reef design and monitoring reefs. The methodological framework is a finding of the research that has many technical components that were tested and combined in this way for the very first time.
In summary, the thesis responds to the urgency and relevance in preserving marine species in tropical reefs during this massive extinction period by offering a differentiated approach towards artificial coral reefs – demonstrating the feasibility of digitally designing such ‘living architecture’ according to multiple context and performance parameters. It also provides an in-depth critical discussion of computational design and architecture in the context of ecosystem regeneration and Planetary Thinking. In that respect, the thesis functions as both theoretical and practical background for computational design, ecology and marine conservation – not only to foster the design of artificial coral reefs technically but also to provide essential criteria and techniques for conceiving them.
Keywords: Artificial coral reefs, computational modelling, high precision underwater monitoring, ecology in design.
Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.
Cultural Heritage on Mobile Devices: Building Guidelines for UNESCO World Heritage Sites' Apps
(2021)
Technological improvements and access provide a fertile scenario for creating and developing mobile applications (apps). This scenario results in a myriad of Apps providing information regarding touristic destinations, including those with a cultural profile, such as those dedicated to UNESCO World Heritage Sites (WHS). However, not all of the Apps have the same efficiency. In order to have a successful app, its development must consider usability aspects and features aligned with reliable content. Despite the guidelines for mobile usability being broadly available, they are generic, and none of them concentrates specifically into cultural heritage places, especially on those placed in an open-air scenario. This research aims to fulfil this literature gap and discusses how to adequate and develop specific guidelines for a better outdoor WHS experience. It uses an empirical approach applied to an open-air WHS city: Weimar and its Bauhaus and Classical Weimar sites. In order to build a new set of guidelines applied for open-air WHS, this research used a systematic approach to compare literature-based guidelines to industry-based ones (based on affordances), extracted from the available Apps dedicated to WHS set in Germany. The instructions compiled from both sources have been comparatively tested by using two built prototypes from the distinctive guidelines, creating a set of recommendations collecting the best approach from both sources, plus suggesting new ones the evaluation.
Multi-user virtual reality systems enable collocated as well as distributed users to perform collaborative activities in immersive virtual environments. A common activity in this context is to move from one location to the next as a group to explore the environment together. The simplest solution to realize these multi-user navigation processes is to provide each participant with a technique for individual navigation. However, this approach entails some potentially undesirable consequences such as the execution of a similar navigation sequence by each participant, a regular need for coordination within the group, and, related to this, the risk of losing each other during the navigation process.
To overcome these issues, this thesis performs research on group navigation techniques that move group members together through a virtual environment. The presented work was guided by four overarching research questions that address the quality requirements for group navigation techniques, the differences between collocated and distributed settings, the scalability of group navigation, and the suitability of individual and group navigation for various scenarios. This thesis approaches these questions by introducing a general conceptual framework as well as the specification of central requirements for the design of group navigation techniques. The design, implementation, and evaluation of corresponding group navigation techniques demonstrate the applicability of the proposed framework.
As a first step, this thesis presents ideas for the extension of the short-range teleportation metaphor, also termed jumping, for multiple users. It derives general quality requirements for the comprehensibility of the group jumping process and introduces a corresponding technique for two collocated users. The results of two user studies indicate that sickness symptoms are not affected by user roles during group jumping and confirm improved planning accuracy for the navigator, increased spatial awareness for the passenger, and reduced cognitive load for both user roles.
Next, this thesis explores the design space of group navigation techniques in distributed virtual environments. It presents a conceptual framework to systematize the design decisions for group navigation techniques based on Tuckman's model of small-group development and introduces the idea of virtual formation adjustments as part of the navigation process. A quantitative user study demonstrates that the corresponding extension of Multi-Ray Jumping for distributed dyads leads to more efficient travel sequences and reduced workload. The results of a qualitative expert review confirm these findings and provide further insights regarding the complementarity of individual and group navigation in distributed virtual environments.
Then, this thesis investigates the navigation of larger groups of distributed users in the context of guided museum tours and establishes three central requirements for (scalable) group navigation techniques. These should foster the awareness of ongoing navigation activities as well as facilitate the predictability of their consequences for all group members (Comprehensibility), assist the group with avoiding collisions in the virtual environment (Obstacle Avoidance), and support placing the group in a meaningful spatial formation for the joint observation and discussion of objects (View Optimization). The work suggests a new technique to address these requirements and reports on its evaluation in an initial usability study with groups of five to ten (partially simulated) users. The results indicate easy learnability for navigators and high comprehensibility for passengers. Moreover, they also provide valuable insights for the development of group navigation techniques for even larger groups.
Finally, this thesis embeds the previous contributions in a comprehensive literature overview and emphasizes the need to study larger, more heterogeneous, and more diverse group compositions including the related social factors that affect group dynamics.
In summary, the four major research contributions of this thesis are as follows:
- the framing of group navigation as a specific instance of Tuckman's model of small-group development
- the derivation of central requirements for effective group navigation techniques beyond common quality factors known from single-user navigation
- the introduction of virtual formation adjustments during group navigation and their integration into concrete group navigation techniques
- evidence that appropriate pre-travel information and virtual formation adjustments lead to more efficient travel sequences for groups and lower workloads for both navigators and passengers
Overall, the research of this thesis confirms that group navigation techniques are a valuable addition to the portfolio of interaction techniques in multi-user virtual reality systems. The conceptual framework, the derived quality requirements, and the development of novel group navigation techniques provide effective guidance for application developers and inform future research in this area.
Mitigating Risks of Corruption in Construction: A theoretical rationale for BIM adoption in Ethiopia
(2021)
This PhD thesis sets out to investigate the potentials of Building Information Modeling (BIM) to mitigate risks of corruption in the Ethiopian public construction sector. The wide-ranging capabilities and promises of BIM have led to the strong perception among researchers and practitioners that it is an indispensable technology. Consequently, it has become the frequent subject of science and research. Meanwhile, many countries, especially the developed ones, have committed themselves to applying the technology extensively. Increasing productivity is the most common and frequently cited reason for that.
However, both technology developers and adopters are oblivious to the potentials of BIM in addressing critical challenges in the construction sector, such as corruption. This particularly would be significant in developing countries like Ethiopia, where its problems and effects are acute. Studies reveal that bribery and corruption have long pervaded the construction industry worldwide. The complex and fragmented nature of the sector provides an environment for corruption. The Ethiopian construction sector is not immune from this epidemic reality. In fact, it is regarded as one of the most vulnerable sectors owing to varying socio-economic and political factors. Since 2015, Ethiopia has started adopting BIM, yet without clear goals and strategies. As a result, the potential of BIM for combating concrete problems of the sector remains untapped. To this end, this dissertation does pioneering work by showing how collaboration and coordination features of the technology contribute to minimizing the opportunities for corruption. Tracing loopholes, otherwise, would remain complex and ineffective in the traditional documentation processes.
Proceeding from this anticipation, this thesis brings up two primary questions: what are areas and risks of corruption in case of the Ethiopian public construction projects; and how could BIM be leveraged to mitigate these risks? To tackle these and other secondary questions, the research employs a mixed-method approach. The selected main research strategies are Survey, Grounded Theory (GT) and Archival Study. First, the author disseminates an online questionnaire among Ethiopian construction engineering professionals to pinpoint areas of vulnerability to corruption. 155 responses are compiled and scrutinized quantitatively. Then, a semi-structured in-depth interview is conducted with 20 senior professionals, primarily to comprehend opportunities for and risks of corruption in those identified highly vulnerable project stages and decision points. At the same time, open interviews (consultations) are held with 14 informants to be aware of state of the construction documentation, BIM and loopholes for corruption in the country. Consequently, these qualitative data are analyzed utilizing the principles of GT, heat/risk mapping and Social Network Analysis (SNA). The risk mapping assists the researcher in the course of prioritizing corruption risks; whilst through SNA, methodically, it is feasible to identify key actors/stakeholders in the corruption venture. Based on the generated research data, the author constructs a [substantive] grounded theory around the elements of corruption in the Ethiopian public construction sector. This theory, later, guides the subsequent strategic proposition of BIM. Finally, 85 public construction related cases are also analyzed systematically to substantiate and confirm previous findings.
By ways of these multiple research endeavors that is based, first and foremost, on the triangulation of qualitative and quantitative data analysis, the author conveys a number of key findings. First, estimations, tender document preparation and evaluation, construction material as well as quality control and additional work orders are found to be the most vulnerable stages in the design, tendering and construction phases respectively. Second, middle management personnel of contractors and clients, aided by brokers, play most critical roles in corrupt transactions within the prevalent corruption network. Third, grand corruption persists in the sector, attributed to the fact that top management and higher officials entertain their overriding power, supported by the lack of project audits and accountability. Contrarily, individuals at operation level utilize intentional and unintentional 'errors’ as an opportunity for corruption.
In light of these findings, two conceptual BIM-based risk mitigation strategies are prescribed: active and passive automation of project audits; and the monitoring of project information throughout projects’ value chain. These propositions are made in reliance on BIM’s present dimensional capabilities and the promises of Integrated Project Delivery (IPD). Moreover, BIM’s synchronous potentials with other technologies such as Information and Communication Technology (ICT), and Radio Frequency technologies are topics which received a treatment. All these arguments form the basis for the main thesis of this dissertation, that BIM is able to mitigate corruption risks in the Ethiopian public construction sector. The discourse on the skepticisms about BIM that would stem from the complex nature of corruption and strategic as well as technological limitations of BIM is also illuminated and complemented by this work. Thus, the thesis uncovers possible research gaps and lays the foundation for further studies.
The computational analysis of argumentation strategies is substantial for many downstream applications. It is required for nearly all kinds of text synthesis, writing assistance, and dialogue-management tools. While various tasks have been tackled in the area of computational argumentation, such as argumentation mining and quality assessment, the task of the computational analysis of argumentation strategies in texts has so far been overlooked.
This thesis principally approaches the analysis of the strategies manifested in the persuasive argumentative discourses that aim for persuasion as well as in the deliberative argumentative discourses that aim for consensus. To this end, the thesis presents a novel view of argumentation strategies for the above two goals. Based on this view, new models for pragmatic and stylistic argument attributes are proposed, new methods for the identification of the modelled attributes have been developed, and a new set of strategy principles in texts according to the identified attributes is presented and explored.
Overall, the thesis contributes to the theory, data, method, and evaluation aspects of the analysis of argumentation strategies. The models, methods, and principles developed and explored in this thesis can be regarded as essential for promoting the applications mentioned above, among others.
Multi-user projection systems provide a coherent 3D interaction space for multiple co-located users that facilitates mutual awareness, full-body interaction, and the coordination of activities. The users perceive the shared scene from their respective viewpoints and can directly interact with the 3D content.
This thesis reports on novel interaction patterns for collaborative 3D interaction for local and distributed user groups based on such multi-user projection environments. A particular focus of our developments lies in the provision of multiple independent interaction territories in our workspaces and their tight integration into collaborative workflows. The motivation for such multi-focus workspaces is grounded in research on social cooperation patterns, specifically in the requirement for supporting phases of loose and tight collaboration and the emergence of dedicated orking territories for private usage and public exchange. We realized independent interaction territories in the form of handheld virtual viewing windows and multiple co-located hardware displays in a joint workspace. They provide independent views of a shared virtual environment and serve as access points for the exploration and manipulation of the 3D content. Their tight integration into our workspace supports fluent transitions between individual work and joint user engagement. The different affordances of various displays in an exemplary workspace consisting of a large 3D wall, a 3D tabletop, and handheld virtual viewing windows, promote different usage scenarios, for instance for views from an egocentric perspective, miniature scene representations, close-up views, or storage and transfer areas. This work shows that this versatile workspace can make the cooperation of multiple people in joint tasks more effective, e.g. by parallelizing activities, distributing subtasks, and providing mutual support.
In order to create, manage, and share virtual viewing windows, this thesis presents the interaction technique of Photoportals, a tangible interface based on the metaphor of digital photography. They serve as configurable viewing territories and enable the individual examination of scene details as well as the immediate sharing of the prepared views. Photoportals are specifically designed to complement other interface facets and provide extended functionality for scene navigation, object manipulation, and for the creation of temporal recordings of activities in the virtual scene.
A further objective of this work is the realization of a coherent interaction space for direct 3D input across the independent interaction territories in multi-display setups. This requires the simultaneous consideration of user input in several potential interaction windows as well as configurable disambiguation schemes for the implicit selection of distinct interaction contexts. We generalized the required implementation structures into a high-level software pattern and demonstrated its versatility by means of various multi-context 3D interaction tools.
Additionally, this work tackles specific problems related to group navigation in multiuser projection systems. Joint navigation of a collocated group of users can lead to unintentional collisions when passing narrow scene sections. In this context, we suggest various solutions that prevent individual collisions during group navigation and discuss their effect on the perceived integrity of the travel group and the 3D scene. For collaboration scenarios involving distributed user groups, we furthermore explored different configurations for joint and individual travel.
Last but not least, this thesis provides detailed information and implementation templates for the realization of the proposed interaction techniques and collaborative workspaces in scenegraph-based VR systems. These contributions to the abstraction of specific interaction patterns, such as group navigation and multi-window interaction, facilitate their reuse in other virtual reality systems and their adaptation to further collaborative scenarios.
This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.
Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
(2020)
Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.
Abstract In the first part of this research, the utilization of tuned mass dampers in the vibration control of tall buildings during earthquake excitations is studied. The main issues such as optimizing the parameters of the dampers and studying the effects of frequency content of the target earthquakes are addressed.
Abstract The non-dominated sorting genetic algorithm method is improved by upgrading generic operators, and is utilized to develop a framework for determining the optimum placement and parameters of dampers in tall buildings. A case study is presented in which the optimal placement and properties of dampers are determined for a model of a tall building under different earthquake excitations through computer simulations.
Abstract In the second part, a novel framework for the brain learning-based intelligent seismic control of smart structures is developed. In this approach, a deep neural network learns how to improve structural responses during earthquake excitations using feedback control.
Abstract Reinforcement learning method is improved and utilized to develop a framework for training the deep neural network as an intelligent controller. The efficiency of the developed framework is examined through two case studies including a single-degree-of-freedom system and a high-rise building under different earthquake excitation records.
Abstract The results show that the controller gradually develops an optimum control policy to reduce the vibrations of a structure under an earthquake excitation through a cyclical process of actions and observations.
Abstract It is shown that the controller efficiently improves the structural responses under new earthquake excitations for which it was not trained. Moreover, it is shown that the controller has a stable performance under uncertainties.
In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies.
In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.
A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.
The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.
Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.
The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.