Engineering Judgment Research Articles

Research on Well Selection Method for Intermittent Pumping in Oil Wells Based on the Analytic Network Process and Fuzzy Logic

In the later stages of oilfield development, the decline in oil well production and the increase in development costs, attributed to issues such as insufficient liquid supply, necessitate the implementation of intermittent pumping measures. However, current methods for selecting these wells lack comprehensiveness in the decision-making process. This article proposes a novel method for selecting intermittent pumping wells utilizing the analytic network process (ANP) and fuzzy logic. Initial surveys identified the main factors influencing intermittent pumping effectiveness. The ANP was employed to screen and integrate six core factors, including submergence depth and water cut. Subsequently, a fuzzy-logic-based model was developed, incorporating trapezoidal and rectangular membership functions to establish detailed correlations among the factors. The model’s efficacy was validated and tested using real-world data from the oilfield. Results indicate that the model’s assessments of intermittent pumping wells align closely with professional engineering judgments. This approach not only provides clear guidance for well selection but also demonstrates high scalability and adaptability across different oilfields by adjusting membership functions, thereby showcasing significant practical value.

Impact of choice of fragility approaches on seismic risk quantification of nuclear power plants

Development and evaluation of seismic fragility of structures and components is crucial in seismic probabilistic risk assessment of nuclear power plants. Simulation-based fragility approaches are a prevailing trend in the literature, while industry-recommended methodologies rely heavily on engineering judgment and deterministic analysis. In this paper, four critical components are selected, modeled and analyzed as a case study. Their fragilities are evaluated using both state-of-the-art fragility methods and code-recommended methods with approximate models. The impact of the choice of fragility approaches on the fragilities of the components and conditional core damage probability of the plant are assessed. The findings reveal that the recommended approaches employed with approximate models have limitations in estimating the median capacity of complex equipment. While there is a notable variance in the treatment of uncertainty among fragility approaches, its influence on core damage probability remains limited unless the component is the primary contributor to core damage.

Precise Modeling and Analysis of Aviation Power System Reliability via the Aviation Power System Reliability Probability Network Model

This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to develop a precise reliability model that captures system functions and fault logic. By classifying APS components into five distinct structural patterns and mapping them to corresponding nodes in the APS-RPNM, the model is successfully constructed. Specifically, None-Input-to-Multiple-Output components are transformed into two-state nodes, while Multiple-Input-to-None-Output, Single-Input-to-Multiple-Output, and Multiple-Input-to-Single-Output components are mapped to three-state nodes. For Multiple-Input-to-Multiple-Output components, a novel approach employing multiple two-state sub-nodes is adopted to capture their complex functional logic. A case study comparing the performance of the APS-RPNM with the traditional minimal path set method in reliability analysis was conducted. The results demonstrate that the APS-RPNM not only simplifies the model construction process and eliminates errors stemming from subjective engineering judgments but also enables the efficient computation of power supply reliability for all load points in a single inference by integrating all of the components. This significantly improves computational efficiency and system dependency analysis capabilities, highlighting the APS-RPNM’s tremendous potential in optimizing the reliability design of APS.

On population-based structural health monitoring for bridges: Comparing similarity metrics and dynamic responses between sets of bridges

Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for ‘populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that ‘similar’ bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges’ mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted.

Observed seismic performance of buildings and bridges during a major earthquake followed by one other major earthquake in the close vicinity of the first one

AbstractIn the recent 2023 major earthquake triggering one other large earthquake in Anatolia, the poor structural performance of buildings and the satisfactory performance of bridges remained relatively consistent compared to past observed earthquake experiences. Despite numerous building collapses, it is worth noting that no bridges collapsed during the event. Several engineering factors for poor building performance have been identified based on field building inspections in the recent and past earthquakes, such as the misuse of building design software without proper engineering judgment, the utilization of poor-quality construction materials, underestimation of earthquake forces, construction on inadequate soil conditions, design mistakes, and incorrect implementation of high ductility reinforcement details. In the aftermath of the earthquake, it was found that only a small percentage of bridges (1%) were closed, and approximately 50% of them were reopened to traffic within 24 h. Remarkably, no bridge collapses including the modern or old bridges were reported in the affected area. The focus of this paper is to identify the differences in the design philosophy between buildings and bridges and to understand the reasons behind the bridges’ resilience during these rare earthquakes. The study involves analyzing past structural performances of buildings and bridges under different earthquake conditions and design requirements. Analytical results from a survived 90-year-old reinforced concrete arch railroad bridge, highlighting its resilience and design characteristics are also presented.

Slope Stability and Reliability Analysis of Earth Embankment Constructed for the Doubling of Railway Track

Embankments of greater height pose threat to its stability and require geotechnical investigation and intervention. Conventionally, limit equilibrium approach is used to estimate the factor of safety of the slope to assess its stability. Over a period of time, with experience and engineering judgments, a factor of safety of 1.4 is deemed sufficient for Indian practice. In the approach, it is inherently assumed that different sources of uncertainty due to testing errors, model transformation, and inherent variability of the geo-material are taken care of with the use of single value of factor of safety. Still, a question remains, whether the suggested factor of safety is acceptable in a given environment of uncertainty or there is a need to further utilize the probabilistic approach. The present study demonstrates how combination of First Order Reliability Method (FORM), Finite Element Method (FEM) and Response Surface Method (RSM) can be useful in the probabilistic assessment of proposed construction of new earth embankment for the doubling of a railway track. It is further highlighted that conventional factor of safety approach when used in conjunction with probabilistic analysis brings rationality in decision making. Also, based on results of slope stability and reliability analysis, it is proposed to incorporate soil nailing to further improve the stability of the earth embankment.

Investigation of the Data Variability of Network-Level Pavement Condition Data

For pavement management purposes it is essential to have quality data for a pavement network. Here the data collected at the network level are investigated for quality using additional statistical approaches. As a part of the quality assurance, randomly sampled pavement sections are evaluated using an independent verification and validation process, and are compared with the network-level data collected by the data collection vendor on the same pavement sections. If the established criteria are met, with composite condition indices, the vendor data are considered acceptable. These criteria are developed based on engineering judgment and experience. The accepted data are further investigated to determine whether any statistical differences exist in individual distress type, severity, or extent. Investigation reveals no statistical differences based on composite condition indices, distress type, or extent. There is no misclassification of any distress type with any other distress type. However, a few distress types have statistical differences in severity levels. Steps are identified to minimize these differences.

Development of an Integrated Model for Open-Pit-Mine Discontinuous Haulage System Optimization

The selection of the optimal equipment for discontinuous haulage systems is one of the most important decisions that need to be made when an open-pit mine is designed. There are a number of influencing factors, including natural (geological and environmental), technical, economic, and social. Some of them can be expressed numerically, in certain units of measure, while others are descriptive and can be stated by linguistic variables depending on the circumstances of the project. These factors are characterized by a high level of uncertainty, associated with both exploration and mining operations. The experience, knowledge, and expert judgment of engineers and specialists are of key importance for the management of mining processes, consistent with the issues stemming from the dynamic expansion of open-pit mines in space over time. This paper proposes an integrated model that translates all the criteria that affect the selection of the optimal solution into linguistic variables. By employing the multiple-criteria decision-making method and combining it with fuzzy logic, we developed an algorithm that addresses all the above-mentioned uncertainties inherent in various mining processes where the experience of experts forms the basis. The fuzzy analytic hierarchy process is used in order to deal with trending decision problems, such as mining equipment and management system selection. The entire algorithm was applied to a real case study—the Ugljevik East 1 open-pit mine.

Engineering Judgment and Education: An Arendtian Account

This article discusses the meaning of judgment in engineering and engineering education, and it does so by introducing the work of political thinker Hannah Arendt. The argument presents Arendt’s non-cognitivist account of judgment as a counterpoint to prevailing conceptions of engineering judgment. Moreover, it suggests that Arendt’s unique perspective on what it means to be human in the context of modern technoscience is relevant to the discussion on the place of the humanities and liberal arts in engineering education.

Strategic Placement of Accelerometers for Structural Health Monitoring of a Complex Unreinforced Stone Masonry Hindu Mandir

ABSTRACT The BAPS Hindu Mandir, recently constructed in Abu Dhabi, UAE, is a complex unreinforced stone masonry structure built from thousands of sculpted sandstone and marble pieces employing ancient Indian techniques called Shilp Shastra. The entire structure is substantially large with a footprint size of 5,100 m 2 and unique so that it is not covered by modern seismic design standards. Its performance was verified by conducting dynamic field tests presented herein. The most vulnerable substructure was identified based on both engineering judgement and modal analysis of the entire structure employing a detailed 3D finite element model, which was validated via the field experiments. A “local” model was developed for the identified vulnerable substructure which significantly reduced model complexity and allowed to overcome computational limitations. Based on the response of the local model, the relative importance of the sensor locations was determined via a Displacement Index method in addition to a reduction of the total variance of spectral accelerations using conditional probability theory. Through this approach, a methodology for selecting the optimal sensor placement with application on the complex unreinforced stone masonry Hindu Mandir is proposed.

STRUCTURAL FIRE ENGINEERING ON HERITAGE INDUSTRIAL BUILDINGS. STRUCTURAL MEASURES TO GUARANTEE THEIR PERFORMANCE

AbstractIndustrial Architecture and their associated engineering structures have a clear objective. They need to be designed and constructed to allow for a product production independently of their nature. In the early XX century German Architecture, especially with the “Bauhaus” movement and others, such as Pier Luigi Nervi in Italy, provided a great impulse to the concept of Industrial Architecture constituting pieces of art. Today, we find examples of industrial buildings that are excellently preserved, and the management of their changing flexibility demand generate engineering judgement to accommodate new “production options” with a safe performance.

Performance degradation of steel rebar considering corrosion spatial variability in concrete

The corrosion of steel bars is the main issue affecting the durability of reinforced concrete structures, which is nonuniform owing to the material properties and external environmental effects. In this study, the performance degradation of steel rebars in concrete is estimated considering corrosion spatial variability. With the aid of a three-dimensional measurement technique, a finite element model based on the scanning result was established to examine the corrosion effect on the tensile behaviour of the corroded bars, which was validated with the experimental results and digital image correlation observation. To assess the deformation behaviour of the corroded steels, a semi-analytical model was developed based on the cross-sectional area distribution and material properties. Subsequently, the spatial weight loss distributions with different degrees of corrosion were generated using a random field model. The model parameters were determined by combining engineering judgement and estimation of the experimental results. Finally, probabilistic model of degraded performance was established using the deterministic assessment and simulated steel weight loss distribution.

Partial safety factor calibration using surrogate models: An application for running safety of ballasted high-speed railway bridges

Traditionally, regulations employ semi-probabilistic methods with partial safety factors to control design limits. Calibrating these partial safety factors involves estimating the target reliability level and optimizing the partial safety factor values in order to minimize the deviation of the safety index between the considered design scenarios and the target value. This procedure necessitates performing a demanding amount of reliability analyses and is often carried out for simplified design situations. Therefore, high computational costs must be accepted for design problems formulated with complex computational models. This study implements a meta-modeling approach based on active learning in the partial safety calibration procedure, enabling its application to computationally intensive problems. Subsequently, the approach is applied to the running safety of ballasted high-speed railway bridges. This limit state implicitly accounts for the phenomenon of ballast destabilization, the occurrence of which disturbs the load path from the rail level to the bridge structure. The dramatic increase in train operating speeds in recent decades has increased the possibility of this design limit state being violated due to resonance. Despite the evident safety concerns, the adopted safety factors appear to be solely based on engineering judgments rather than calibration through higher-level reliability analysis. Therefore, the proposed calibration method is employed to determine the corresponding partial safety factors for various maximum allowable operating train speeds. The newly calibrated partial safety factors allow for a permissible maximum vertical acceleration of the bridge deck approximately 25% higher than the conventional design approaches. Therefore, incorporating these factors into the design procedure may lead to the construction of lighter bridges.

Determination of uncertainties of geomechanical parameters of metamorphic rocks using petrographic analyses

Geomechanical parameters of intact metamorphic rocks determined from laboratory testing remain highly uncertain because of the great intrinsic variability associated with the degrees of metamorphism. The aim of this paper is to develop a proper methodology to analyze the uncertainties of geomechanical characteristics by focusing on three domains, i.e. data treatment process, schistosity angle, and mineralogy. First, the variabilities of the geomechanical laboratory data of Westwood Mine (Quebec, Canada) were examined statistically by applying different data treatment techniques, through which the most suitable outlier methods were selected for each parameter using multiple decision-making criteria and engineering judgment. Results indicated that some methods exhibited better performance in identifying the possible outliers, although several others were unsuccessful because of their limitation in large sample size. The well-known boxplot method might not be the best outlier method for most geomechanical parameters because its calculated confidence range was not acceptable according to engineering judgment. However, several approaches, including adjusted boxplot, 2MADe, and 2SD, worked very well in the detection of true outliers. Also, the statistical tests indicate that the best-fitting probability distribution function for geomechanical intact parameters might not be the normal distribution, unlike what is assumed in most geomechanical studies. Moreover, the negative effects of schistosity angle on the uniaxial compressive strength (UCS) variabilities were reduced by excluding the samples within a specific angle range where the UCS data present the highest variation. Finally, a petrographic analysis was conducted to assess the associated uncertainties such that a logical link was found between the dispersion and the variabilities of hard and soft minerals.

Applied Bayesian structural health monitoring: Inclinometer data anomaly detection and forecasting

AbstractInclinometer probes are devices that can be used to measure deformations within earthwork slopes. This paper demonstrates a novel application of Bayesian techniques to real‐world inclinometer data, providing both anomaly detection and forecasting. Specifically, this paper details an analysis of data collected from across the entire UK rail network.Inclinometers are a standard tool of geotechnical site monitoring. Data from these instruments is often used in risk analysis and decision‐making. However, discerning anomalous data points and forecasting future behaviour from inclinometer data requires significant ‘engineering judgement’ (subjective appraisal). This is because the observational data is derived from complex physical phenomena and contains complex spatio‐temporal correlations. Additionally, the practical demands of data collected from remote sites over several years tends to introduce systematic errors. These issues make the interpretation of inclinometer data challenging.Practitioners have effectively two goals when processing monitoring data. The first is to identify any anomalous or dangerous movements, and the second is to predict potential future adverse scenarios by forecasting. In this paper we apply Uncertainty Quantification (UQ) techniques by implementing a Bayesian approach to anomaly detection and forecasting for inclinometer data. Subsequently, both costs and risks may be minimised by quantifying and evaluating the appropriate uncertainties. This framework may then act as an enabler for enhanced decision making and risk analysis.We show that inclinometer data can be described by a latent autocorrelated Markov process derived from measurements. This can be used as the transition model of a non‐linear Bayesian filter. This allows for the prediction of system states. This learnt latent model also allows for the detection of anomalies: observations that are far from their expected value may be considered to have ‘high surprisal’, that is they have a high information content relative to the model encoding represented by the learnt latent model.We successfully apply the forecasting and anomaly detection techniques to a large real‐world data set in a computationally efficient manner. Although this paper studies inclinometers in particular, the techniques are broadly applicable to all areas of engineering UQ and Structural Health Monitoring (SHM).

Crash modification factors of rumble strips on horizontal curves of two-lane rural roads: A propensity scores potential outcomes approach

Horizontal curves are known to be more crash-prone than tangent sections particularly with respect to roadway departure crashes. Rumble strips are an effective countermeasure to mitigate various types of roadway departure crashes. While existing studies on the safety effectiveness of rumble strips have primarily used before-after study designs or cross-sectional methods for crash modification factor (CMF) estimation, these methods often suffer from imbalanced datasets and larger standard errors, especially when the sample size is small. To address this, this study applies the propensity score potential outcome (PSPO) framework to estimate CMFs for centerline rumble strips, shoulder rumble strips, and their combined application on horizontal curves. In addition to contributing to the development of CMFs by crash severity, this study also examines the effects of rumble strips on collision types, highlighting their impact on vehicle maneuvering and collision characteristics. The analysis is conducted on horizontal curves on two-lane rural roads in Pennsylvania, utilizing crash data from 2017 to 2021. The PSPO method effectively reduces bias between sites with and without rumble strips, and the resulting statistical models align with engineering judgment. The findings indicate that centerline rumble strips reduce opposite direction sideswipe and head-on crashes but increase run off the road and hit fixed object crashes. Shoulder rumble strips, either alone or in combination with centerline rumble strips, decrease crash frequencies for most types except opposite direction sideswipe and head-on crashes. However, shoulder rumble strips alone are more effective at reducing crash frequencies on horizontal curves than when combined with centerline rumble strips.

Digital geotechnics: from data-driven site characterisation towards digital transformation and intelligence in geotechnical engineering

ABSTRACT Geotechnical engineering is experiencing a paradigm shift towards digital transformation and intelligence, driven by Industry 4.0 and emerging digital technologies, such as machine learning. However, development and application of machine learning are relatively slow in geotechnical practice, because extensive training databases are a key to the success of machine learning, but geotechnical data are often small and ugly, leading to the difficulty in developing a suitable training database required for machine learning. In addition, convincing examples from real projects are rare that demonstrate the immediate added value of machine learning to geotechnical practices. To facilitate digital transformation and machine learning in geotechnical engineering, this study proposes to develop a project-specific training database that leverages on digital transformation of geotechnical workflow and reflects both project-specific data collected from various stages of the geotechnical workflow and domain knowledge in geotechnical practices, such as soil mechanics, numerical analysis principles, and prior engineering experience and judgment. A real ground improvement project is presented to illustrate the proposed method and demonstrate the added value of digital transformation and machine learning in geotechnical practices.

Geotechnical variability of the soils of Quito, Ecuador: a geodatabase study

This paper presents a new geotechnical database for the soils of Quito, Ecuador. The geotechnical database is then used to investigate the best fit probability distributions for the key geotechnical parameters contained in the database. Using the Akaike information criteria for best fit selection, SPT (N), plasticity index, Vs30, peak friction angle (direct shear), and apparent cohesion (triaxial) are best represented by a Weibull distribution. The peak friction angle (triaxial) is best fitted with a truncated normal distribution. The database is also used to develop transformation models to allow for the estimation of more complex geotechnical parameters from intrinsic ones. This analysis shows that the transformation model between Vs30 and SPT (N) has high coefficients of determination and is statistically significant. Finally, the systematic collection of information in the database is used to investigate the assumption, based on engineering judgement by local practitioners, that soil derived from volcanic deposits and volcano-lacustrine sediments in the northern part of Quito has different geotechnical properties with respect to the southern zone of the city beyond the value of shear wave velocity whose difference is embedded in the soil classification map of the seismic code.

Numerical study failure characteristics of natural fracture under induced stress

Hydraulic fracturing is an effective method for developing oil and gas in unconventional reservoirs. The openings of natural fractures directly affect the complexity of the fracture body. The failure characteristics of natural fractures are critical factors in determining their activation by hydraulic fractures. Based on the three-dimensional displacement discontinuity method, a three-dimensional mechanical model of the interaction between hydraulic and natural fractures was established. Based on the shear and tensile failure criteria of the natural fractures, the failure characteristics of different natural fractures were clarified. Research has shown that the failure characteristics of natural fractures are influenced by factors such as three-dimensional stress, spatial distribution, and roughness. After the hydraulic fractures pass through the natural fractures, tensile and shear failures occur simultaneously; the failure of natural fractures is primarily affected by their strike and dip angles. During the process of hydraulic fracture approximation, the shear stress increases as the distance between the hydraulic fracture tip and natural fracture decreases. When the strike angle of the natural fractures near the tips of the hydraulic fractures was greater than 35°, the stress became consistent. As the dip angle of the natural fractures increased, the lowest point of the shear stress gradually moved from the upper to the lower half. This research studies the tensile and shear failure of natural fractures under different conditions, in order to facilitate engineering judgment of the types and characteristics of natural fracture failure. It is of great significance for us to understand the formation mechanism of complex fracture network morphology in volume fracturing.

Effect of refraction and field of view of acrylic viewports of shallow water spherical pressure hull

A shallow water spherical hull for manned submersible has been designed and developed by National Institute of Ocean Technology, India. During the design phase, a detailed study was carried out concerning the conical acrylic viewports, to understand the limit of aperture, field of view through viewports, field of view of seafloor, and reflection. It is also important to understand the effect of refraction due to acrylic and sea water, which is significant for making engineering judgements while collecting samples from the seabed. This study recommends having larger diameter viewports and larger angles between the primary and secondary viewports. A limitation would be that increasing the diameter of the viewport would increase the size of the viewport flange. Highlights The layout of the observation windows, field of view, field of view of the sea floor, and refraction effects are studied Effect of refraction due to acrylic and sea water, which is significant for making engineering judgements while collecting samples from the seabed The distortion depends on the viewing angle, thickness of the viewport window, and object distance inside the sea water Minimum overlapping regions in the fields of view of viewports need to be maintained so that two crews can see the objects together which fall in the common field of view