Elaborate Models Research Articles

Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring

Visible and near infrared spectroscopy (VIS-NIR) is increasingly being transferred from laboratory to industry for in-line and portable applications in various domains. By intensively using VIS-NIR spectroscopy, some abnormal observations may certainly arise. It is then important to properly handle outliers to elaborate effective prediction models. The objective of this study is to investigate the potential of using a robust method called Roboost-PLSR to improve prediction model performances for a viticulture application. This work focuses on a case study to predict sugar content in grape berries of three different grape varieties of Vitis Vinifera in a maturity monitoring context. Hyperspectral images were acquired of grape berries of Syrah, Fer-Servadou and Mauzac varieties. Reference measurements of sugar levels were made in the laboratory by densimetric baths. Performances of RoBoost-PLSR models were compared to performances of reference models using Partial Least Square Regression (PLSR). Reference prediction criteria using PLSR were obtained for all varieties with these following values: Syrah (Rp2 ​= ​0.971; RMSEp ​= ​5.36 ​g/L), Fer-servadou (Rp2 ​= ​0.788; RMSEp ​= ​11.69 ​g/L) and Mauzac (Rp2 ​= ​0.690; RMSEp ​= ​15.61 ​g/L). Prediction qualities are improved with RoBoost-PLSR: Syrah (Rp2 ​= ​0.990; RMSEp ​= ​3.14 ​g/L), Fer-Servadou (Rp2 ​= ​0.848; RMSEp ​= ​10.20 ​g/L) and Mauzac (Rp2 ​= ​0.927; RMSEp ​= ​7.58 ​g/L). Results confirm that Roboost-PLSR method allows a better consideration of outliers within the calibration set.

Use of 2-D and 3-D unsteady RANS in the computation of wall bounded buoyant flows

The present study numerically investigates the performance of a range of RANS turbulence closures with different near-wall treatments in the prediction of natural convection flows. Comparisons are performed of both 2-D and 3-D steady and time-dependent computations in differentially heated cavities, which involve very simple geometries, but give rise to complex flow physics of relevance to cooling applications, including safety features of nuclear reactors. The turbulence closures assessed include eddy-viscosity and second-moment closures and involve low-Reynolds-number versions (LRN) which fully resolve the viscous sublayer, as well as high-Reynolds-number versions (HRN) which adopt wall function treatments to prescribe boundary conditions for the wall shear stress and heat flux.In the 2-D computations of the tall rectangular cavity, there are only small deviations in the predictions of different models, and these are mainly confined to the local Nusselt number comparisons. Among the LRN models, the second-moment EBRSM is in good agreement with the data, while the LRN eddy-viscosity models under-estimate the Nusselt number levels. The HRN models, when used with the standard log-law-based wall function, over-estimate the Nusselt number. During the course of the present investigation, a new, more general, version of the Analytical Wall Function (AWF) has been developed and introduced to the open-source CFD code utilised. Here, this more general numerical wall treatment brings the HRN k-ε predictions of the Nusselt number to very close agreement with the experimental data.In the 2-D square cavity computations, the simulations focus on a high Rayleigh number (Ra) of 1011, for which DNS data has recently become available. At such high Ra value all the models are challenged due to the unsteadiness of the flow and the presence of a largely stationary and laminar core, and thin turbulent boundary layers. Assessment of different 3-D configurations of the same square cavity demonstrates that the effect of the turbulent mixing in the third (z) direction is significant, but can be taken into account by introducing a cavity depth of D=0.15H, matching that used in the DNS study. The deviations in the predictions of the different models are stronger than those observed in the corresponding rectangular cavity comparisons, especially for the local Nusselt number distribution. As is also the case in the rectangular cavity, the eddy-viscosity models under-estimate the Nusselt number, while the EBRSM with the more elaborate turbulent heat flux model once more is closest to the DNS data. All HRN models with the log-law wall function severely under-estimate the Nusselt number levels. Introduction of the buoyancy-adapted AWF leads to substantial predictive improvements. The HRN AWF approach is thus shown to be a reliable and cost-effective modelling strategy for natural convection flows.

An implicit integration scheme with consistent tangent modulus for Leblond\u2019s model of transformation-induced plasticity in steels

Thermomechanical treatments involving solid-state phase transformations play an important role for the manufacturing of functional and reliable components in many engineering applications. Accordingly, numerical investigation and optimization of such processes require considering thermoelastoplasticity under the influence of ongoing transformations and in particular the impact of transformation-induced plasticity (TRIP). While a number of elaborate plasticity models have been proposed for the description of TRIP, none of them seem to have received much prevalence in applications due to their complexity or hard to determine model parameters. Instead, the overwhelming majority of applied research either relies on simplistic formulations dating back to early phenomenological approaches or neglects TRIP altogether. In this work, we therefore provide an accessible, straightforward and easy-to-implement solution scheme for the TRIP model proposed by Leblond et al. which, despite being widely recognized, is hardly ever employed in full form. Specifically, we employ implicit backward-Euler integration and an elastic–plastic operator split approach to update the stresses in order to obtain a simple and concise algorithm for which we then derive the corresponding consistent tangent modulus. Furthermore, the work contains an application of the solution scheme to a symmetrically cooled plate and an in-depth discussion of the influence of TRIP by means of this tractable numerical example. Specifically, we highlight the discrepancies arising in transient and residual stresses and strains compared to the conventional J_2-plasticity approach where the phase transformation is accounted for merely by adapting the yield strength of the compound.

Modeling entrepreneurial team faultlines: Collectivism, knowledge hiding, and team stability

New ventures often rely on entrepreneurial teams as a key resource for success. However, presence of multiple teams in entrepreneurial firms may create unexpected faultlines. With a categorization–elaboration model, this study tests the relationships of entrepreneurial team faultlines with knowledge hiding and team stability, as well as the potentially moderating role of team collectivism, as an important aspect of team dynamics. A test of the proposed theoretical model, among 66 entrepreneurial teams in China, reveals that social faultlines promote team knowledge hiding, but informational faultlines diminish it. Team knowledge hiding also mediates the process by which social and informational faultlines affect team stability. Team collectivism conditions these relationships in notable ways.

A virtual chemistry model for soot prediction in flames including radiative heat transfer

Modeling soot formation is a very difficult task which has been assessed by the numerical combustion community. Numerical simulations of turbulent sooting flames remain very challenging due to the complexity of soot formation phenomena, which implies development of elaborate soot models often too computationally demanding, or simplified soot models that are limited to a small range of operating conditions of interest. Here an innovative optimized global approach called virtual chemistry is proposed. It consists of a mathematical formalism including virtual species and virtual reactions, whose thermochemical parameters are optimized in order to describe a combustion system. Thermochemical properties are trained through a genetic algorithm, employing a learning database made of reference flame elements. This makes it possible to reproduce multiple combustion regimes and operating conditions. The virtual chemistry approach reproduces then the structure of hydrocarbon-air flames, as well as predicting specific user-defined pollutant species. The objective of this article is to extend the virtual chemistry methodology for soot formation prediction. As the radiative heat losses are important in sooting flames, a new radiative virtual model is also developed to account for them. The final reduced chemistry consists of 12 virtual species and 6 virtual reactions considerably decreasing the computational time compared to detailed chemistry models. Simulations of 1-D and 2-D laminar ethylene-air sooting flames are performed to evaluate the virtual soot and radiative models. Temperature, soot volume fraction and radiative heat losses are well predicted and are in good agreement with the reference data.

Seismic design method and plastic hinge of square steel tube confined reinforced concrete (STRC) columns

Steel tube confined reinforced concrete columns have the advantages of high bearing capacity, good ductility and convenient construction, which meet the requirements of "building industrialization", so they are widely used in high-rise buildings. Based on the plastic-damage constitutive model of concrete and elastic-plastic mixed strengthen constitutive model of steel, finite element software ABAQUS was used to build an elaborate 3D finite element solid model and perform pseudo-static analysis of square steel tube confined RC(STRC) columns. The model considered the confinement effect of the steel tube exerting on the core concrete, as well as reasonable boundary conditions and loading modes. Good agreement was achieved between the finite element analysis results and the existing quasi static experimental results regarding to the failure modes, load-displacement hysteretic curves and load-displacement skeleton curves. Based on the validated model, 136 full scale model examples were established and extensive parametric analysis was conducted. The results show that the confinement of external steel tube can reduce the peak compressive strain and increase the peak compressive stress of concrete, thus delaying the failure of RC column and improving its bearing capacity and ductility effectively. Besides, the failure displacement angle and ultimate bearing capacity increase when the thickness of steel tube increases. Finally, the seismic design method including reasonable thickness and hoop index of the steel tube were proposed for 5 material strength matching and different axial compression ratios. A method to judge the plastic hinge based on concrete strain and steel tube strain was proposed, as well as a practical calculation formula of plastic hinge length. This study can provide basis and support for the seismic design of frame columns.

Efficient mitigation of high-speed train vibrations on adjacent reinforced concrete buildings

The detrimental impact of vibrations on adjacent reinforced concrete (RC) buildings due to high-speed train (HST) passage is investigated in this work. For this purpose, an elaborate numerical model, initially validated with free-field data, has been developed. Subsequently, a 4-story RC building is added at varying distances from track to assess the developing vibrations levels on the structure. Furthermore, an efficient mitigation measure is applied, i.e., single or double trenches filled with expanded polystyrene (EPS) geofoam are placed close to the track, in order to minimize the adverse impact of HST-induced vibrations on residents’ health.

Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.

BackgroundEffective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient’s hospitalization period may support the making of judicious decisions regarding bed management.ObjectiveFirst, this study aims to develop a machine learning (ML)–based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services.MethodsWe set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features.ResultsWe experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes.ConclusionsIn this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.

MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments

This paper is an initial endeavor to bridge the gap between powerful Deep Reinforcement Learning methodologies and the problem of exploration/coverage of unknown terrains. Within this scope, MarsExplorer, an openai-gym compatible environment tailored to exploration/coverage of unknown areas, is presented. MarsExplorer translates the original robotics problem into a Reinforcement Learning setup that various off-the-shelf algorithms can tackle. Any learned policy can be straightforwardly applied to a robotic platform without an elaborate simulation model of the robot’s dynamics to apply a different learning/adaptation phase. One of its core features is the controllable multi-dimensional procedural generation of terrains, which is the key for producing policies with strong generalization capabilities. Four different state-of-the-art RL algorithms (A3C, PPO, Rainbow, and SAC) are trained on the MarsExplorer environment, and a proper evaluation of their results compared to the average human-level performance is reported. In the follow-up experimental analysis, the effect of the multi-dimensional difficulty setting on the learning capabilities of the best-performing algorithm (PPO) is analyzed. A milestone result is the generation of an exploration policy that follows the Hilbert curve without providing this information to the environment or rewarding directly or indirectly Hilbert-curve-like trajectories. The experimental analysis is concluded by evaluating PPO learned policy algorithm side-by-side with frontier-based exploration strategies. A study on the performance curves revealed that PPO-based policy was capable of performing adaptive-to-the-unknown-terrain sweeping without leaving expensive-to-revisit areas uncovered, underlying the capability of RL-based methodologies to tackle exploration tasks efficiently.

A Component-Based Model for Novel Modular Connections with Inbuild Component

A modular steel structure building has obvious advantages in reducing construction period and protecting the environment due to its unique construction method, so it is widely used in modern construction. However, the modular building connection design and modeling are mostly based on the traditional connection research results. To address this issue, the paper developed a component-based model for novel modular connections with an inbuild component. First of all, the comprehensive parameter study was implemented using elaborate finite element models. Then the component-based model for novel modular connections was developed, and the force-deformation response of each component was determined using the finite element method. Thirdly, assembly of all components to overall rotational joint and the simplified finite element model of modular connections was obtained. Finally, comparison between simplified and refined finite element was conducted, the results showed that the proposed model can predict the mechanical behavior of modular building connections within the acceptable margin of error.

A design experiment on technology‐based learning progress feedback in a graduate‐level online course

Our literature review has revealed that the definition of learning progress as a function of formative feedback remains unclear, which continues to the lack of well-reasoned learning progress feedback (LPF) strategies. Also, few scholars have empirically examined student perceptions of informed learning progress in solving a complex problem. The current study aims (a) to generate insights that elaborate LPF in a technology-enhanced learning environment and (b) to gain better knowledge about LPF design for technology-enhanced formative assessment and feedback. In this design experiment, we proposed LPF design strategies in five categories. Then, we experimented with two LPF prototypes to see how students reacted to the LPF features and how students perceived the value of LPF feedback. In the context of a graduate-level online course with 35 student participants, we performed a mixed-data analysis. Results indicate that students benefited from the two LPF designs to navigate their learning progress. Students' LPF preference appeared to relate to their feedback literacy regarding revision behavior, cognitive progress, and emotional reaction. The current findings lay the groundwork for elaborate LPF models that are pedagogically effective and adaptive to diverse student needs. The proposed LPF design typology and further design suggestions can help advance LPF design experiments and improve technology-based formative feedback that enables students to pursue mastery.

Translational Model of Cortical Premotor-Motor Networks.

Deciphering the physiological patterns of motor network connectivity is a prerequisite to elucidate aberrant oscillatory transformations and elaborate robust translational models of movement disorders. In the proposed translational approach, we studied the connectivity between premotor (PMC) and primary motor cortex (M1) by recording high-density electroencephalography in humans and between caudal (CFA) and rostral forelimb (RFA) areas by recording multi-site extracellular activity in mice to obtain spectral power, functional and effective connectivity. We identified a significantly higher spectral power in β- and γ-bands in M1compared to PMC and similarly in mice CFA layers (L) 2/3 and 5 compared to RFA. We found a strong functional β-band connectivity between PMC and M1 in humans and between CFA L6 and RFA L5 in mice. We observed that in both humans and mice the direction of information flow mediated by β- and γ-band oscillations was predominantly from PMC toward M1 and from RFA to CFA, respectively. Combining spectral power, functional and effective connectivity, we revealed clear similarities between human PMC-M1 connections and mice RFA-CFA network. We propose that reciprocal connectivity of mice RFA-CFA circuitry presents a suitable model for analysis of motor control and physiological PMC-M1 functioning or pathological transformations within this network.

Cognitive diversity and team creativity: Effects of demographic faultlines, subgroup imbalance and information elaboration

Confusion in previous studies over how and when cognitive diversity results in higher or lower performance has led to an examination of the influence of a range of moderator variables. Our study provides new theoretical insights into this field of research by extending the categorization–elaboration model (CEM) through an examination of the interaction effects of multiple convergent demographic diversities on information elaboration, team processes and creativity. Based on comparative fit and an in-group projection model of social categorization theory, the moderating influence of demographic faultlines and subgroup imbalance are also investigated. Using hierarchical regression analysis (HRA), empirical support from 453 team members nested within 91 teams across five organizations was found for the mediating role of team information elaboration on the relationship between cognitive diversity and team creativity. Furthermore, both demographic faultlines and subgroup balance were found to moderate the relationship between cognitive diversity and team information elaboration. Our study provides additional clarity concerning processes underlying the positive effects of cognitive diversity on team performance.

The Delphic forward guidance puzzle in New Keynesian models

When the central bank has information that can help the private sector better predict the future, should it communicate such information to the public? In purely forward-looking New Keynesian models, such Delphic forward guidance unambiguously reduces ex ante welfare by increasing the variability of inflation and the output gap. We call this phenomenon the Delphic forward guidance puzzle. In more elaborate models with endogenous state variables, a combination of Delphic forward guidance and preemptive policy actions may improve welfare. However, full information revelation is generally not optimal and what information needs to be revealed is highly model-dependent.

Elaborate Modeling and Fragility Assessment of a Multiframe PC Box-Girder Bridge with Intermediate Hinges in California

Multiframe PC box-girder bridge with intermediate hinges is a kind of bridge having complex structural characters, which is very quintessential in California. In this study, a typical bridge was adopted to establish a nonlinear dynamic model through OpenSees platform. Intermediate hinge and inhibiting devices in it were elaborately simulated. Meanwhile, pushover analysis was used to reinstate a specimen of column test, which has the similar ratio of reinforcement to the typical bridge, and the hysteretic model parameters of the longitudinal steels inside columns were obtained. The damage indexes of column and hinge, which are primary components, under different limit states were acquired by moment-curvature analysis. Taking into account the uncertainty, nonlinear time-history analysis of the bridge was carried out through a suite of synthetic ground motions. Subsequently, a probabilistic seismic demand model was developed, and fragility curves were further focused on. According to fragility assessment, the conclusion shows that columns and hinge restrainers exhibit high fragility, and bridge system fragility is gradually determined by column fragility along with aggravating of the damage state. Unseating of girder can hardly occur at abutments and intermediate hinges. Moderate limit state could be exceeded in the positions of plug-type concrete structures in intermediate hinges, which tends to create transverse and vertical cracks, furthermore causing reinforcements yield. It indicates that it would severely underestimate the seismic fragility of intermediate hinges without considering the elaborate simulation of hinges.

A survey of remote attestation in Internet of Things: Attacks, countermeasures, and prospects

The explosive growth of the Internet of Things (IoT) devices is an inevitable trend, especially considering the fact that 5G technology facilitates numerous services building on IoT devices. IoT devices deliver great convenience to our daily lives; nevertheless, they are becoming attractive attacking targets. Compromised IoT devices can result in the exposure of user privacy, damage to network security, or even threats to personal safety. In a rush for convenience and marketability, the security of these devices is usually less considered during production and even ignored. Under these circumstances, Remote Attestation (RA) becomes a valuable security service. It outsources the computation and verification burden to a resource-rich party, e.g., server, to ease its on-device implementation, making it suitable for protocol extensions.In this paper, we investigate the state-of-the-art RA schemes from different perspectives, aiming to offer a comprehensive understanding of this security service. Specifically, we summarize the basis of RA. We set up an elaborate adversarial model by systematizing existing RA schemes. Then we put forward the evaluation criteria from protection capability, performance, network adaptability, and attestation quality. According to the adversarial model, we classify existing RA schemes into five categories to show the various characteristics. A comparison of representative proposals enables readers to adopt and design suitable protocols in different application scenarios. Finally, we discuss some open challenges and provision prospects for future research.

Enhancing sustainable supply chain management performance through alliance portfolio diversity: the mediating effect of sustainability collaboration

PurposeWhile strategic alliances is a concept increasingly discussed in the field of sustainable supply chain management (SSCM), an emerging and more crucial concept regarding alliances—namely, the alliance portfolio—is mostly ignored in the SSCM context. Mainly drawing on the categorisation–elaboration model (CEM), this research develops a three-layer model to explore the effects of three alliance portfolio diversity facets on the three triple-bottom-line SSCM performances through the mediation of sustainability collaboration.Design/methodology/approachThe field data are collected from 321 Vietnamese manufacturers. Scale accuracy is assessed through the confirmatory factor analysis method. Hierarchical linear regressions are applied to test the proposed model and hypotheses.FindingsPartner, governance, and functional alliance portfolio diversities have a U-shaped, inverted U-shaped, and positive linear effect, respectively, on sustainability collaboration. Sustainability collaboration is in turn found to enhance the SSCM performances in terms of economic, environmental, and social.Originality/valueThis research introduced a new theoretical lens, CEM, to the SSCM field. It also provided findings that can help firms to manage their alliance portfolios more dynamically in terms of the nature and diversity level of the portfolio and in a way that adds to the triple bottom line through the mediating effect of sustainability collaboration.

Auditory, Visual, and Cross-Modal Mismatch Negativities in the Rat Auditory and Visual Cortices.

When the brain tries to acquire an elaborate model of the world, multisensory integration should contribute to building predictions based on the various pieces of information, and deviance detection should repeatedly update these predictions by detecting “errors” from the actual sensory inputs. Accumulating evidence such as a hierarchical organization of the deviance-detection system indicates that the deviance-detection system can be interpreted in the predictive coding framework. Herein, we targeted mismatch negativity (MMN) as a type of prediction-error signal and investigated the relationship between multisensory integration and MMN. In particular, we studied whether and how cross-modal information processing affected MMN in rodents. We designed a new surface microelectrode array and simultaneously recorded visual and auditory evoked potentials from the visual and auditory cortices of rats under anesthesia. Then, we mapped MMNs for five types of deviant stimuli: single-modal deviants in (i) the visual oddball and (ii) auditory oddball paradigms, eliciting single-modal MMN; (iii) congruent audio-visual deviants, (iv) incongruent visual deviants, and (v) incongruent auditory deviants in the audio-visual oddball paradigm, eliciting cross-modal MMN. First, we demonstrated that visual MMN exhibited deviance detection properties and that the first-generation focus of visual MMN was localized in the visual cortex, as previously reported in human studies. Second, a comparison of MMN amplitudes revealed a non-linear relationship between single-modal and cross-modal MMNs. Moreover, congruent audio-visual MMN exhibited characteristics of both visual and auditory MMNs—its latency was similar to that of auditory MMN, whereas local blockage of N-methyl-D-aspartic acid receptors in the visual cortex diminished it as well as visual MMN. These results indicate that cross-modal information processing affects MMN without involving strong top-down effects, such as those of prior knowledge and attention. The present study is the first electrophysiological evidence of cross-modal MMN in animal models, and future studies on the neural mechanisms combining multisensory integration and deviance detection are expected to provide electrophysiological evidence to confirm the links between MMN and predictive coding theory.

Barbarigenesis and the collapse of complex societies: Rome and after.

"Barbarism" is perhaps best understood as a recurring syndrome among peripheral societies in response to the threats and opportunities presented by more developed neighbors. This article develops a mathematical model of barbarigenesis-the formation of "barbarian" societies adjacent to more complex societies-and its consequences, and applies the model to the case of Europe in the first millennium CE. A starting point is a game (developed by Hirshleifer) in which two players allocate their resources either to producing wealth or to fighting over wealth. The paradoxical result is that a richer and potentially more powerful player may lose out to a poorer player, because the opportunity cost of fighting is greater for the former. In a more elaborate spatial model with many players, the outcome is a wealth-power mismatch: central regions have comparatively more wealth than power, peripheral regions have comparatively more power than wealth. In a model of historical dynamics, a wealth-power mismatch generates a long-lasting decline in social complexity, sweeping from more to less developed regions, until wealth and power come to be more closely aligned. This article reviews how well this model fits the historical record of late Antiquity and the early Middle Ages in Europe both quantitatively and qualitatively. The article also considers some of the history left out of the model, and why the model doesn't apply to the modern world.

Dynamic behaviors of buried reinforced concrete pipelines with gasketed bell-and-spigot joints subjected to tunnel blasting vibration

Reinforced concrete pipelines with gasketed bell-and-spigot joints are widely spread in urban areas. The jointed concrete pipe is vulnerable subjected to blasting vibration induced by adjacent tunnel blasting excavation, which may lead to serious consequences to residents and tunnel construction. However, research on the dynamic response of jointed pipe under impact loads is vague. For analyzing the impact of blasting vibration on adjacent buried reinforced concrete pipelines with gasketed bell-and-spigot joints, full-scale blasting experiments with the pipeline buried in the silty clay layer which is a typical condition in the Wuhan area are conducted. Specifically, vibration velocity and dynamic strain variation characteristics of the pipe segments and joints subject to blasting vibration are obtained. In addition, LS-DYNA with an elaborate pipe joint model and field measuring data verification is adopted to better show the detailed response of pipe segments and joints. Comparative analysis is conducted between the continuous pipe and jointed pipe to clarify the influence of pipe joint on the whole pipeline. In the end, the countermeasures of jointed pipeline protection against blasting vibration induced by blasting excavation are presented.