Engineering Case Study Research Articles

Characterization Model Research on Deformation of Arch Dam Based on Correlation Analysis Using Monitoring Data

Deformation is the most direct indicator of structural state changes in arch dams. Therefore, numerous deformation monitoring points are typically arranged on arch dams to obtain deformation data from each point. Considering the complex relationships between the deformation at each monitoring point, this study focuses on the internal structural relationships and information fusion within the dam. The Pearson correlation coefficient is used as a similarity index to determine significant linear correlations between the measuring points. Ward’s cluster analysis method is then applied to group these points based on their similarities. To identify measuring points with strong nonlinear correlations, the Maximum Information Coefficient (MIC) method is employed. By integrating these linear and nonlinear correlations, a model is constructed to characterize the deformation at specific measurement points using data from strongly correlated points. The effectiveness of this model is verified through a concrete engineering case study, offering a novel approach for analyzing arch dam deformations.

The Use of English Relative Clauses by Indonesian Authors: A Case Study in Business, Engineering, and Science Journal Articles

The ability to effectively use relative clauses is pivotal in English journal articles, as it allows authors the flexibility to modify various noun phrases within sentences to convey clearer and more precise writing. This research explores the strategies employed by Indonesian authors from three distinct disciplines - business, engineering, and science. To accomplish this, we analyzed thirty papers published in Electrical Engineering and Computer Science, Makara Journal of Science, and Gadjahmada International Journal of Business. In total, 1453 relative clauses were collected and analyzed. A cross-classification analysis was performed to examine the distribution of relative clauses. The findings of the study indicate a preference among Indonesian writers to use non-reduced relative clauses, specifically those that serve as restrictive modifiers for objects. We also observe that the past participle is employed with greater frequency than the present participle. The results reveal that Indonesian authors prefer non-reduced relative clauses over reduced ones, opt for restrictive clauses over non-restrictive ones, and consistently favor relativizing the subject (S-form) in their relative clauses over the object (O-form). Our results also show that the choice of relative clauses is subject-specific, influenced by the distinct communicative requirements and preferences within each field. Our examination of relative clauses has provided insights into the syntactic choices, including their reduction status, restrictiveness, the use of participles, connectors, and the syntactic category modified by the clause.

CROSS-CULTURAL CONFLICT ON ORGANIZATIONAL PERFORMANCE IN THE INTERNATIONAL MANAGEMENT OF CONSTRUCTION COMPANIES

This article focuses on the issue of cross-cultural management in the internationalization strategy of Chinese construction companies, especially in the Kazakhstan market. Through an exploratory approach, an in-depth case study of Pacific engineering & construction company limited (PECC), a Chinese construction company in Kazakhstan, this research provides rich insights into cross-cultural management in internationalization strategy. Based on data from 51 questionnaires, this article analyzes the impact of cross-cultural conflict on organizational performance in the internationalization management of construction companies. The results of the study show that the relationship between cross-cultural conflict and organizational performance is not a simple linear relationship, but an «inverted U-shaped» relationship. Both too high and too low cross-cultural conflict are detrimental to organizational performance, but appropriate cross-cultural conflict is conducive to organizational performance. This study contributes to the limited literature on internationalization of Chinese construction companies. At the same time, it provides a reference for the internationalization of other small and medium-sized enterprises in Kazakhstan. Key words: Cross-cultural conflict; Organizational performance; International management; Enterprise internationalization; Construction company.

Time-Varying Stability Analysis of the Trenching Construction Process of Diaphragm Wall

The stability of underground diaphragm walls is crucial for ensuring the safety and integrity of trench excavations in geotechnical engineering. This study addresses this critical issue by proposing a novel destabilization mechanism based on a sliding body model specifically designed for diaphragm wall trenching operations. The research employs an analytical framework rooted in soil mechanics and plasticity theory, utilizing limit equilibrium analysis to develop a method for calculating the minimum required slurry density and corresponding safety factor for trench stability. The study compares two distinct approaches to slurry density computation, analyzing their sensitivity to various influencing factors. Theoretical findings are validated through multiple real-world engineering case studies. Comparative analysis demonstrates the superiority of the proposed method, particularly in assessing trench stability within clay layers. Key variables influencing the safety factor are identified, including trench length, slurry density, soil friction angle, and the relative height difference between slurry and groundwater levels. Results indicate that actual slurry densities observed in practice consistently fall within the bounds predicted by the theoretical calculations. This research contributes a valuable theoretical framework to the field of diaphragm wall construction, offering improved accuracy in stability assessments and potentially enhancing safety in geotechnical engineering projects.

Research and Modification on the Park-Ang Damage Index for Railway Rectangular Piers

ABSTRACT This study addressed the issue of distortion in evaluating the seismic damage state of railway bridge piers using the original Park-Ang damage index. By constructing a parameterized co-simulation program with two finite element models, OpenSees and Abaqus, five sets of flexural failure and five sets of flexural-shear failure rectangular pier quasi-static test results were selected to validate the rationality of the calculation program. Four hundred and eighty parameterized calculations were conducted using this program. The combination coefficient β and critical damage index DI within the Park-Ang damage index were modified using nonlinear regression algorithm and convolutional neural network algorithm. The adaptability of the damage index calculation was ultimately verified through an engineering case study of a simply supported beam bridge. The research findings are as follows: 1) The main factor influencing β is the longitudinal reinforcement ratio, while the axial compression ratio, shear span ratio, volumetric stirrup ratio, aspect ratio, concrete strength, and steel strength are secondary parameters influencing β. 2) Compared to the nonlinear regression algorithm, the convolutional neural network algorithm can better establish a calculation model between pier structural parameters and β, clarifying their relationship. 3) It is recommended to use critical values of 0.11, 0.29, 0.49, and 1.00 for assessing pier damage as no, slight, moderate, severe, and collapse damage when using the damage index calculation model. 4) The Park-Ang index corrected by convolutional neural networks demonstrates improved accuracy and computational stability, making it suitable for practical assessment of seismic damage in bridge structures.

From ensemble learning to deep ensemble learning: A case study on multi-indicator prediction of pavement performance

Recently, Big data analytics approaches were combined with emerging machine learning techniques, which can provide more sophisticated insights into information-intensive activity. Compared to traditional shallow-architecture machine learning algorithms, Deep learning could excavate more potential information from the raw features. However, its powerful representational capacity relies on the support of enormous samples. The ensemble trees system performs superior on small sample problems due to better generalization capacity. To merge both benefits of deep learning and ensemble tree system, this paper developed a deep ensemble algorithm applied to multiple indicators prediction of pavement performance, including International Roughness Index and pavement 3-layer modulus. The deep ensemble algorithm is developed by merging a deep neural network (DNN) with the decision manifold property of the decision trees (TabNet) into a cascade ensemble system, combined with a sliding window algorithm to extract dependency information from raw data. During the training stage, the Bayesian Optimization Algorithm (BOA) is used to search for the optimal combination of sub-decision makers in the cascade ensemble. And equipped with GPU, it can speed up by 2.6–4.0 times. In the case study of pavement engineering, with sufficient training samples, it can achieve an average accuracy of 98.74 %, higher than DNN (97.49 %) and XGBoost (96.12 %) in predicting pavement indicators. With insufficient training samples, it can achieve an accuracy improvement of 12 % than XGBoost (75 %) and 24.5 % than DNN (62.5 %).

Bayesian modeling and optimization for split-plot experiments with multiple responses

In many industrial processes, cost or time constraints make some input variables harder to change or control than others. An appropriate experimental design method is restricted randomization, which results in split-plot experiments. Empirical models that connect multiple quality characteristics with input variables play a crucial role in robust parameter design for split-plot experiments. At present, many modeling methods typically adopt the single response model for analyzing industrial processes in the split-plot experiments without considering correlation among multiple responses, correlation among whole plots, and uncertainty of model parameters. However, ignoring these issues can lead to poor product or process design. To solve these issues, this paper suggests a novel Bayesian modeling and optimization approach. We first construct a Bayesian multi-response linear mixed-effects model and obtain the posterior distribution for model parameters by employing Bayesian theorem. Then, the Gibbs sampling procedure is employed for the estimation of model parameters. Finally, the overall weighted desirability optimization function meeting the specification is developed to avoid acquiring ideal input settings with outliers. A simulation and an engineering case study demonstrate the validity of the proposed method. In comparison to existing methods, the optimization results given the proposed method are more robust and reliable.

Multidisciplinary Reliability Design Optimization Modeling Based on SysML

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.

Application of the Rosenblatt transformation in First-Order System Reliability approximations

The reliability assessment of structural systems presents a significant challenge in structural engineering. A commonly employed approximation is the First-Order System Reliability Method (FOSRM), which estimates system reliability using the FORM component reliabilities and sensitivity factors. An essential step in FORM involves transforming the random vector X into the standard vector U, often using the Rosenblatt transformation (RT). Several studies demonstrated that different conditioning orders in the RT yield different FORM component results. This study investigates how these differences on component level propagate into the FOSRM system level. We conducted several typical engineering case studies with various failure probabilities, system sizes, and dependency structures (Gaussian and Frank Copula). For the Frank Copula, different Rosenblatt conditioning orders systematically yielded different FOSRM results, with most cases showing differences between 10% and 30% in estimated failure probability. For some systems, these differences increased with system size, suggesting that greater variations might be observed for larger systems. Notably, systems with Gaussian Copula functions also proved vulnerable to the Rosenblatt conditioning order when different components were assessed with different conditioning orders. The observed differences were larger than previously reported and should be carefully considered in uniform safety assessments.

A deep learning approach based on domain generalization for mooring tension prediction in floating structures

Generalization remains a significant challenge in the application of deep learning methods, particularly in predicting mooring line tension for moored floating structures. The dynamic and complex nature of marine environments makes accurate prediction of mooring line tension a formidable task. Existing deep learning models, constrained by the data distribution of their training sets, typically perform well within the scope of these datasets. However, their limited generalization capability becomes apparent when confronted with unknown and changing marine conditions, resulting in a decline in prediction accuracy. To address this issue, this paper proposes a Domain Generalization (DG) model approach, which incorporates a distribution matching regularization term to the model's loss function, aimed at reducing distributional disparities between data from different domains. The proposed method was compared with existing Deep Neural Network (DNN) model method through validation with engineering case studies. The results indicate that the DG model method significantly surpasses the DNN model in terms of generalization capabilities. The mooring line tension prediction model developed herein holds potential for constructing digital twins, offering near real-time data support for moored floating engineering projects. This facilitates timely and accurate decision-making in response to environmental changes.

Settlement estimation during foundation excavation using pattern analysis and explainable AI modeling

With the rapid expansion of underground engineering, accurate settlement estimation during foundation excavation using monitoring data has gained prominence. Previous studies have typically overlooked data patterns, relying solely on time-series models, which yielded limited accuracy and short-term predictability. To address these issues, this paper performs a thorough pattern analysis covering both the temporal and spatial properties, and proposes a spatiotemporal modeling method grounded in explainable artificial intelligence technique. A real-world engineering case study is conducted to validate the effectiveness of the proposed method. Counterintuitively, the results reveal a weak temporal effect within the settlement data, with only the last step playing a dominant role, whereas the spatial correlation among measuring points is notably more significant. Compared to conventional models, the proposed method consistently outperforms, achieving significantly higher R2 scores and excelling in long-term estimation with reductions of at least 78% in RMSE, 80% in MAE, and 72% in MAPE.

Evaluation of grouting effectiveness for Semi-Rigid pavement base layer cracks based on Time-Frequency domain signal characteristics of 3D GPR

This study aims to address the deficiency of rapid, comprehensive, and non-destructive methods for evaluating the effectiveness of grouting treatment on semi-rigid pavement base layer cracks. By integrating radar simulation with empirical measurements, we establish a comprehensive evaluation method for the grouting treatment effectiveness of pavement base layer cracks. Initially, we propose a GprMax simulation of crack models with varying grout-filled volumes, analyzing the time–frequency domain signal characteristics before and after grouting. Based on data sensitivity, thirteen time–frequency domain signal characteristics are selected as grading evaluation indicators. Subsequently, relying on an engineering case study, we conduct a graded evaluation of the grouting effect on base layer cracks, analyzing the correlation and weight of evaluation indicators to derive a comprehensive evaluation method. Field coring results confirm the accuracy of the comprehensive graded evaluation method.

Streamlining Project Management in the Production Sector: A Case Study of Industrial Engineering

The objective of the research is to pinpoint issues in project management, as this is crucial for the development of a successful project. There is a need to expand the existing knowledge in project management, as it contributes to enhancing a company’s project management system. Additionally, the study presents effective remedies for project management challenges. Initially, we introduce the specific project, followed by an examination of the current state of production processes. In this context, we endeavour to propose solutions. The concluding section focuses on evaluating these solutions, summarizing the study, and achieving the study’s objective. These outcomes contribute to cost savings and ensure maximum customer satisfaction.

A Hybrid Prediction Model for Rock Reservoir Bank Slope Deformation Considering Fractured Rock Mass Parameters

Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical simulations stand out due to their well-defined physical and mechanical concepts, and their ability to make effective predictions with limited monitoring data. The predictive accuracy of hybrid models is closely tied to the precise determination of rock mass mechanical parameters in structural numerical simulations. However, rock masses in rock slopes are characterized by intersecting geological structural planes, resulting in reduced strength and the creation of multiple fracture flow channels. These factors contribute to the heterogeneous, anisotropic, and size-dependent properties of the macroscopic deformation parameters of the rock mass, influenced by the coupling of seepage and stress. To improve the predictive accuracy of the hybrid model, this study introduces the theory of equivalent continuous media. It proposes a method for determining the equivalent deformation parameters of fractured rock mass considering the coupling of seepage and stress. This method, based on a discrete fracture network (DFN) model, is integrated into the hybrid prediction model for rock slope deformation. Engineering case studies demonstrate that this approach achieves a high level of prediction accuracy and holds significant practical value.

A novel multi-objective optimization strategy based on vibrating particle system algorithm applied to chemical process design

Chemical process design is a highly interesting field within the chemical engineering community due to the numerous potential applications it offers. Traditionally, this problem involves multiple objectives and constraints related to mass, energy, and momentum balances, as well as limitations based on environmental, physical, and operating conditions. Over the past few decades, various multi-objective optimization strategies incorporating evolutionary algorithms have been proposed and tested using test cases of varying complexity. In this study, we propose a new Multi-objective Optimization Vibrating Particle System (MOVPS) algorithm. This numerical strategy extends the Vibrating Particle System Algorithm to a multi-objective context by incorporating three operators into the original algorithm: Pareto dominance, crowding distance, and a mutation strategy. To expedite the convergence process and avoid local optima, an external repository is considered. We evaluate the performance of the proposed methodology by applying it to mathematical functions (ZDT functions) and chemical process design problems. Regarding the ZDT functions, the computational cost (number of objective function evaluations and the processing time) was not increased considering the proposed methodology, as well as the values of convergence and diversity metrics are in agreement with those calculated considering other multi-objective optimization strategies. For most engineering case studies, it was possible to verify that the proposed methodology was able to obtain good estimates for the Pareto curve without increasing the computational cost. However, for the industrial styrene reactor problem it was possible to improve the Pareto curve and reduce the computational cost in relation to other multi-objective optimization algorithms.

Introducing pre-training workshop sessions to enhance learning in multidisciplinary environments: a case study in computer-aided engineering and design

Introducing pre-training workshop sessions to enhance learning in multidisciplinary environments: a case study in computer-aided engineering and design

A Linear Optimization for Slope Leveling of Ground-Mounted Centralized Photovoltaic Sites

Slope leveling is essential for the successful implementation of ground-mounted centralized photovoltaic (PV) plants, but currently, there is a lack of optimization methods available. To address this issue, a linear programming approach has been proposed to optimize PV slope leveling. This method involves dividing the field into blocks and grids and using hyperbolic paraboloids to simulate the design surface. By programming in MATLAB, the globally optimal solution for PV slope leveling can be calculated. Engineering case studies have demonstrated that this optimization method can achieve significant cut-and-fill volume savings ranging from 58% to 78%, when compared to the traditional segmented plane method. Additionally, the effectiveness of the optimization method improves with larger site areas and more complex terrains. A parameter analysis considering slope ratio, grid size, and block size reveals that grid size has a minimal impact on cut-and-fill volume, while slope ratio and block size have a significant influence. For typical PV projects, the recommended ranges of slope ratio, grid size, and block size are 3–7%, 5–20 m, and 30–50 m, respectively, for slope leveling design. In summary, the proposed linear optimization method provides an optimal slope leveling scheme for ground-mounted centralized PV plants, with convenient operation and fast computation.

A hybrid numerical model for horizontal ground heat exchanger

The horizontal ground heat exchanger (HGHE) possesses a complicated heat transfer mechanism as its extensive spatial scale, long operational duration, and vulnerability to meteorological conditions. Consequently, the long-period simulations of HGHE usually involve significant computational costs, posing challenges for its dynamic optimization implementation. Therefore, this study initially established and validated a conventional numerical (full-order) model for HGHE as the reference model. Subsequently, a hybrid model was developed using the proposed adaptive proper orthogonal decomposition (POD) method. By analyzing the influential characteristics, the study identified the solution strategy and the key parameter values for adaptive POD, followed by the generality tests. The hybrid model proved to successfully mitigate the issue of error accumulation commonly associated with native POD extrapolation. Finally, employing a long-running engineering case study, the accuracy and the solution efficiency of the hybrid model were compared against those of the conventional (full-order) model. The results demonstrated that the hybrid model maintained computational accuracy at a comparable level while exhibiting a computational efficiency 326 % higher than that of the conventional (full-order) model, without requiring additional computational resources. This study can provide efficient modeling support for the dynamic optimization design of HGHE.

OIL: an industrial case study in language engineering with Spoofax

AbstractDomain-specific languages (DSLs) promise to improve the software engineering process, e.g., by reducing software development and maintenance effort and by improving communication, and are therefore seeing increased use in industry. To support the creation and deployment of DSLs, language workbenches have been developed. However, little is published about the actual added value of a language workbench in an industrial setting, compared to not using a language workbench. In this paper, we evaluate the productivity of using the Spoofax language workbench by comparing two implementations of an industrial DSL, one in Spoofax and one in Python, that already existed before the evaluation. The subject is the Open Interaction Language (OIL): a complex DSL for implementing control software with requirements imposed by its industrial context at Canon Production Printing. Our findings indicate that it is more productive to implement OIL using Spoofax compared to using Python, especially if editor services are desired. Although Spoofax was sufficient to implement OIL, we find that Spoofax should especially improve on practical aspects to increase its adoptability in industry.

Hiring Discrimination Under Pressures to Diversify: Gender, Race, and Diversity Commodification across Job Transitions in Software Engineering

White, male-dominated professions in the United States are marked with substantial gender and racial inequality in career advancement, yet they often face pressures to increase diversity. In these contexts, are theories of employer biases based on gender and racial stereotypes sufficient to explain patterns of hiring discrimination during common career transitions in the external labor market? If not, how and why do discrimination patterns deviate from predictions? Through a case study of software engineering, we first draw from a large-scale audit study and demonstrate unexpected patterns of hiring screening discrimination: while employers discriminate in favor of White men among early-career job applicants seeking lateral positions, for both early-career and senior workers applying to senior jobs, Black men and Black women face no discrimination compared to White men, and White women are preferred. Drawing on in-depth interviews, we explain these patterns of discrimination by demonstrating how decision-makers incorporate diversity value—applicants’ perceived worth for their contribution to organizational diversity—into hiring screening decisions, alongside biases. We introduce diversity commodification as the market-based valuative process by which diversity value varies across job level and intersectional groups. This article offers important implications for our understanding of gender, race, and employer decision-making in modern U.S. organizations.