Hasil untuk "Hydraulic engineering"

K. Zhang, Chao‐sheng Tang, Ningjun Jiang et al.

The microbial‑induced carbonate precipitation (MICP), as an emerging biomineralization technology mediated by specific bacteria, has been a popular research focus for scientists and engineers through the previous two decades as an interdisciplinary approach. It provides cutting-edge solutions for various engineering problems emerging in the context of frequent and intense human activities. This paper is aimed at reviewing the fundaments and engineering applications of the MICP technology through existing studies, covering realistic need in geotechnical engineering, construction materials, hydraulic engineering, geological engineering, and environmental engineering. It adds a new perspective on the feasibility and difficulty for field practice. Analysis and discussion within different parts are generally carried out based on specific considerations in each field. MICP may bring comprehensive improvement of static and dynamic characteristics of geomaterials, thus enhancing their bearing capacity and resisting liquefication. It helps produce eco-friendly and durable building materials. MICP is a promising and cost-efficient technology in preserving water resources and subsurface fluid leakage. Piping, internal erosion and surface erosion could also be addressed by this technology. MICP has been proved suitable for stabilizing soils and shows promise in dealing with problematic soils like bentonite and expansive soils. It is also envisaged that this technology may be used to mitigate against impacts of geological hazards such as liquefaction associated with earthquakes. Moreover, global environment issues including fugitive dust, contaminated soil and climate change problems are assumed to be palliated or even removed via the positive effects of this technology. Bioaugmentation, biostimulation, and enzymatic approach are three feasible paths for MICP. Decision makers should choose a compatible, efficient and economical way among them and develop an on-site solution based on engineering conditions. To further decrease the cost and energy consumption of the MICP technology, it is reasonable to make full use of industrial by-products or wastes and non-sterilized media. The prospective direction of this technology is to make construction more intelligent without human intervention, such as autogenous healing. To reach this destination, MICP could be coupled with other techniques like encapsulation and ductile fibers. MICP is undoubtfully a mainstream engineering technology for the future, while ecological balance, environmental impact and industrial applicability should still be cautiously treated in its real practice.

Hilde Nijland, Sumit Vij, Jeroen Warner

Urban wetlands are essential for sustaining biodiversity, mitigating floods and supporting livelihoods, yet they are among the planet’s most threatened ecosystems. In Guwahati, a rapidly urbanising capital city in Northeast India, wetlands are a critical urban commons. They are shared spaces managed and used by urban communities, and are vital to collective wellbeing. They currently face threats from urban agglomeration, and there remains a significant gap in the understanding of how different and often contesting discourses shape perceptions, uses and governance of these wetlands. This research, therefore, addresses the key question: How are the discourses surrounding Guwahati’s wetlands contested? Employing critical discourse analysis, data collection methods included semi-structured interviews with residents across Guwahati and field observations in the two wetland areas of Deepor Beel and Silsako Beel. Findings suggest that the state (municipal and other line agencies) primarily frames wetlands as a resource for driving urban development – a discourse that is reinforced by the state’s practices. This reflects a growing detachment from these ecosystems and a clear progression towards state control and commodification, where wetlands are transformed from urban commons and meaningful 'places’ into abstract, commercialised 'spaces'. These discourses are used by both the state and several residents, but are challenged by environmentally conscious residents and civil society groups advocating for wetland preservation. These contestations illustrate the complex and conflicting values attributed to urban wetlands. Currently, the state’s modernity agenda seems to take precedence, resulting in their increasing commodification.

Mayuranath SureshKumar, Hanumanthrao Kannan

Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

X. Zhuang, Shuwei Zhou, M. Sheng et al.

Abstract In the hydraulic fracturing of natural rocks, understanding and predicting crack penetrations into the neighboring layers is crucial and relevant in terms of cost-efficiency in engineering and environmental protection. This study constitutes a phase field framework to examine hydraulic fracture propagation in naturally-layered porous media. Biot's poroelasticity theory is used to couple the displacement and flow field, while a phase field method helps characterize fracture growth behavior. Additional fracture criteria are not required and fracture propagation is governed by the equation of phase field evolution. Thus, penetration criteria are not required when hydraulic fractures reach the material interfaces. The phase field method is implemented within a staggered scheme that sequentially solves the displacement, phase field, and fluid pressure. We consider the soft-to-stiff and the stiff-to-soft configurations, where the layer interface exhibits different inclination angles θ . Penetration, singly-deflected, and doubly-deflected fracture scenarios can be predicted by our simulations. In the soft-to-stiff configuration, θ = 0 ° exhibits penetration or symmetrical doubly-deflected scenarios, and θ = 15 ° exhibits singly-deflected or asymmetric doubly-deflected scenarios. Only the singly-deflected scenario is obtained for θ = 30 ° . In the stiff-to-soft configuration, only the penetration scenario is obtained with widening fractures when hydraulic fractures penetrate into the soft layer.

Mohammad Mirzaahmadi, Hamed Sarkardeh, Ali Katebi et al.

This study identifies 35 risks related to the supply chain of concrete dams, of which 25 were selected as critical risks using the Delphi method. Risks were categorized into three groups based on their sources including environmental, network and organizational risks. The Failure Mode and Effect Analysis (FMEA) method was then applied to evaluate the significance of risks in three areas of construction cost, time and quality. The results revealed that 44% of risks in the construction cost category were critical, while 21% of risks in construction time and 12% in construction quality were found to be critical. Appropriate responses to mitigate critical risks were provided, including the use of alternative supply resources, rigorous project scheduling, and diversified financial strategies. This study highlights the importance of risk management in improving project performance in terms of cost, time, and quality, and emphasizes the need for effective communication and continuous risk monitoring throughout the project lifecycle.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Daniel Mendez, Paris Avgeriou, Marcos Kalinowski et al.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Kevin Hermann, Sven Peldszus, Jan-Philipp Steghöfer et al.

Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially, maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control -- to ensure the security of their software. Although security features are usually available in libraries, integrating security features requires writing and maintaining additional security-critical code. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement and which ones may require custom implementation, and the implications for maintenance. As a result, we currently rely on assumptions that are largely based on common sense or individual examples. However, to provide them with effective solutions, researchers need hard empirical data to understand what practitioners need and how they view security -- data that we currently lack. To fill this gap, we contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and what challenges practitioners face. Based on the empirical data gathered, we provide insights into engineering practices and validate four common assumptions.

Tim Aebersold, Soheyl Massoudi, Mark D. Fuge

Engineering complex systems (aircraft, buildings, vehicles) requires accounting for geometric and performance couplings across subsystems. As generative models proliferate for specialized domains (wings, structures, engines), a key research gap is how to coordinate frozen, pre-trained submodels to generate full-system designs that are feasible, diverse, and high-performing. We introduce Generative Latent Unification of Expertise-Informed Engineering Models (GLUE), which orchestrates pre-trained, frozen subsystem generators while enforcing system-level feasibility, optimality, and diversity. We propose and benchmark (i) data-driven GLUE models trained on pre-generated system-level designs and (ii) a data-free GLUE model trained online on a differentiable geometry layer. On a UAV design problem with five coupling constraints, we find that data-driven approaches yield diverse, high-performing designs but require large datasets to satisfy constraints reliably. The data-free approach is competitive with Bayesian optimization and gradient-based optimization in performance and feasibility while training a full generative model in only 10 min on a RTX 4090 GPU, requiring more than two orders of magnitude fewer geometry evaluations and FLOPs than the data-driven method. Ablations focused on data-free training show that subsystem output continuity affects coordination, and equality constraints can trigger mode collapse unless mitigated. By integrating unmodified, domain-informed submodels into a modular generative workflow, this work provides a viable path for scaling generative design to complex, real-world engineering systems.

Haijun Wang, Yuhang Yang, Yaohua Guo et al.

Based on the 10 MW OO-Star semi-submersible floating platform, this study proposes internal and external heave plates to enhance its stability and explores their influence on the platform’s hydrodynamic characteristics. The platform’s structural behavior is analyzed in both frequency and time domains using numerical simulation methods. The study investigates the effects of the porosity and number of holes (with an equal porosity) of the inner heave plate and the opening angle (with the equal area) of the external heave plate on the platform’s hydrodynamic characteristics, ultimately obtaining the optimal arrangement for the inner and external heave plates. Results indicate that the best scheme involves a 10% porosity with 16 holes, which reduces the heave amplitude by 5.7% compared to the original structure. Additionally, reducing the opening angle of the external heave plate increases the added mass and natural period in the heave and pitch directions of the platform. At an opening angle of 140°, the added mass in the heave direction can increase by 25.2% compared to the original structure. Overall, the internal and external heave plates effectively reduce the heave and pitch amplitude of the platform under severe sea conditions.

Luca Cucchi, Sophie Damy, Ciro Gioia et al.

With corrections transmitted through the E6 signal, the Galileo High Accuracy Service (HAS) provides the information necessary to execute a stand-alone precise point positioning algorithm in real time. Once fully operational, the service aims to deliver an accuracy of 20 cm and 40 cm (at the 95% confidence level) in the horizontal and vertical channels, respectively. While most of the current literature focuses on analyzing the performance of HAS in static and open-sky signal reception scenarios, this study presents the results of tests conducted in both static and dynamic conditions, including open-sky and urban canyon scenarios. The tests clearly demonstrate that utilizing HAS corrections leads to a significant reduction in positioning error across all tested environments. Furthermore, a specific analysis of HAS message availability in a harsh environment indicates that the corrections obtained from the signal in space are available approximately 95% of the time during dynamic scenario tests.

Felix Schmid, Jorge Leandro

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

Dawid Kudas, Agnieszka Wnęk, Ewelina Zając

Soil sealing is a threat to soil and its ecosystem services. One of the main drivers of soil sealing is land degradation resulting from the expansion of urban areas, where it leads to such problems as the growing risk of flooding and local inundations, urban heat islands, or water shortages. The article focuses on analyses and quantification of the general degree of soil sealing in 2012–2018 in eight functional urban areas (FUA) in Poland, taking into account their division into the urban core (UC) and the commuting zone (CZ). We used the high resolution layer imperviousness density (HRL IMD) data to quantify soil sealing as well as data on land cover and land use with different spatial resolutions, i.e. from the European Urban Atlas project (UA) and the National Database of Topographic Objects (BDOT10k) to quantify artificial surfaces. The research determined the spatial differentiation of UCs and CZs in terms of the degree of soil sealing. We further observed higher average growth of sealed land in CZs. Quantitative and spatial analyses determined the spatial patterns of soil sealing in the FUA in Poland. Soil sealing intensified from 2012 to 2018. The process should be expected to continue in the coming years in light of the continuous transformation of vegetated areas into artificial ones. The conclusions should be considered valuable for the implementation of the spatial policy concerning sustainable land use and soil protection in suburban areas.

Degfie Teku, Nega Kesete, Abebaw Abebe

Soil erosion is the most challenging and major environmental problems in the Upper Meki Sub catchment. Therefore, this work aims to determine the relative influences of erosion governing factors and to estimate the annual soil loss in the sub catchment area using RUSLE model. The model variables including rainfall erosivity (R), soil erodibility (K), topography (LS), cover and management (C), and support practices (P) were implemented into the GIS environment and a layer of each of these factors was prepared. The estimated value of R, K, LS, C, and P for the sub catchment area ranges from 512 to 604 MJ mm ha-1 h-1 yr-1, 0.137 to 0.169 tons/ha, 0 to 59, 0.001 to 0.4 and 0.10 – 1.00 respectively. The raster values of all these factors were multiplied by using GIS calculator. Based on the results from GIS raster calculation and RUSLE model, the total annual potential soil loss from the sub catchment area is about 2,756,540 tons per year with a mean estimated soil loss rate of 28.12 + 34.77 t/ha/yr and the total actual annual soil loss is 492929 tons with an estimated erosion rate of 37.05 + 46.7 t/ha/yr. Rainfall is identified as the most sensitive factor of soil erosion in the sub catchment area. Our estimation of soil erosion provides notional basses that the area needs immediate action to sustain the soil. Nevertheless, further research on severity analysis and area prioritization, and sediment loss estimation in this watershed is highly recommended to develop practical way of conserving practices.

Jukka Ruohonen, Kalle Hjerppe

Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.

Nadia Alshahwan, Mark Harman, Inna Harper et al.

In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.

Marvin Wyrich, Justus Bogner

LinkedIn is the largest professional network in the world. As such, it can serve to build bridges between practitioners, whose daily work is software engineering (SE), and researchers, who work to advance the field of software engineering. We know that such a metaphorical bridge exists: SE research findings are sometimes shared on LinkedIn and commented on by software practitioners. Yet, we do not know what state the bridge is in. Therefore, we quantitatively and qualitatively investigate how SE practitioners and researchers approach each other via public LinkedIn discussions and what both sides can contribute to effective science communication. We found that a considerable proportion of LinkedIn posts on SE research are written by people who are not the paper authors (39%). Further, 71% of all comments in our dataset are from people in the industry, but only every second post receives at least one comment at all. Based on our findings, we formulate concrete advice for researchers and practitioners to make sharing new research findings on LinkedIn more fruitful.

X. Bian, Wei Zhang, Xiaozhao Li et al.

Jun Zhang, Qian Yu, Yuwei Li et al.