Fitness Metrics Research Articles

Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors

Scan-to-BIM technology can help construction stakeholders to obtain accurate BIM models for building information management and construction progress monitoring. The current practice of scan-to-BIM relies on laser scanning to capture a building's as-built condition, followed by processing scanned point clouds to generate BIM, which is laborious and time-consuming. This study presents a new robot-assisted mobile laser scanning approach using a legged robot and a solid-state LiDAR sensor, for automated 3D reconstruction and point cloud semantic segmentation. The proposed new approach involves integrating the Simultaneous Localisation and Mapping (SLAM) algorithm with robot motion control and pathfinding for 3D mapping to obtain the complete point cloud data. To ensure the completeness and density of scanned point clouds, new scanning fitness metrics and a grid-based direction-by-direction algorithm are designed for robot pathfinding. Then, an enhanced dynamic window approach is established to integrate the motion control of the legged robot with SLAM for reconstructing dense point clouds. Lastly, semantic segmentation is performed using ResPointNet++ to segment architectural and structural components. Experimental results show that the mobile scanning approach achieves a decent performance in point cloud completeness and density, with an overall mIoU of 81.75% for semantic segmentation. The ablation test demonstrates that the proposed approach is superior to the method that does not integrate the robot motion with SLAM. The proposed approach contributes to the efficient collection of high-quality point clouds for building interiors, with an aim to boost building information management.

Factor Structure of the Bar-On Emotional Quotient Inventory in Youth on the Autism Spectrum

Examination of emotional intelligence (EI) of autistic individuals has gained popularity. These efforts have included the BarOn Emotional Quotient Inventory, Youth Version (BarOn EQ-i YV); however, this measure was not standardized with this population and so its utility and the accuracy of its factor structure for this population is questionable. This study examined how well the factor structure as represented by a sample of autistic children and youth aligns with that described in the measure’s technical manual to clarify considerations for use. Results indicate poor metrics of model fit. Some factors were significantly correlated, though this was attenuated somewhat upon correction for multiple analyses. Two items from the interpersonal factor were negatively loaded, suggesting they should be subtracted from other items in that factor (in contrast to the standardized model). Implications for EI construct validity, understanding of EI in autism, and use of EI measures for this population are discussed.

Forecasting ICU Census by Combining Time Series and Survival Models.

Retrospective observational study. We developed the algorithm using data from the Medical-Surgical ICU (MSICU) at University Hospital, London, Canada and validated using data from the Critical Care Trauma Centre (CCTC) at Victoria Hospital, London, Canada. Adult patient admissions (7,434) to the MSICU and (9,075) to the CCTC from 2015 to 2021. None. We developed an Autoregressive integrated moving average time series model that forecasts patients arriving in the ICU and a survival model using MODS, NEMS, and other factors to estimate patient LOS. The models were combined to create an algorithm that forecasts ICU census for planning horizons ranging from 1 to 7 days. We evaluated the algorithm quality using several fit metrics. The root mean squared error ranged from 2.055 to 2.890 beds/d and the mean absolute percentage error from 9.4% to 13.2%. We show that this forecasting algorithm provides a better fit when compared with a moving average or a time series model that directly forecasts ICU census. Additionally, we evaluated the performance of the algorithm using data during the global COVID-19 pandemic and found that the error of the forecasts increased proportionally with the number of COVID-19 patients in the ICU. It is possible to develop accurate tools to forecast ICU census. This type of algorithm may be important to clinicians and managers when planning ICU capacity as well as staffing and surgical demand planning over a short time horizon.

Kimma: flexible linear mixed effects modeling with kinship covariance for RNA-seq data.

The identification of differentially expressed genes (DEGs) from transcriptomic datasets is a major avenue of research across diverse disciplines. However, current bioinformatic tools do not support covariance matrices in DEG modeling. Here, we introduce kimma (Kinship In Mixed Model Analysis), an open-source R package for flexible linear mixed effects modeling including covariates, weights, random effects, covariance matrices, and fit metrics. In simulated datasets, kimma detects DEGs with similar specificity, sensitivity, and computational time as limma unpaired and dream paired models. Unlike other software, kimma supports covariance matrices as well as fit metrics like Akaike information criterion (AIC). Utilizing genetic kinship covariance, kimma revealed that kinship impacts model fit and DEG detection in a related cohort. Thus, kimma equals or outcompetes current DEG pipelines in sensitivity, computational time, and model complexity. Kimma is freely available on GitHub https://github.com/BIGslu/kimma with an instructional vignette at https://bigslu.github.io/kimma_vignette/kimma_vignette.html.

Impact of different factors affecting Urdu teaching in Pakistan: A Confirmatory Factor

In Pakistani society, Urdu is widely recognized as the official language of the country as well as one of the widely spoken languages. Urdu is regarded as one of the symbols of national and cultural identity and plays an important role in social life. This study used CFA to examine whether three factors in Urdu teaching (curriculum and teaching materials, teachers and teaching methods, and learning environment) had a significant impact on Urdu teaching. Confirmatory Factor Analysis (CFA) is a multivariate statistical method designed to verify whether a set of potential factors proposed by researchers can explain the variation in observed variables. CFA was used in this study to build a three-factor model that includes three factors: curriculum and teaching materials, teachers and teaching methods, and learning environment. The study will use sample data to perform parameter estimates of the model and use model fit metrics (fit index and standardized residuals) to assess how well the model fits.

Selecting the Number and Labels of Topics in Topic Modeling: A Tutorial

Topic modeling is a type of text analysis that identifies clusters of co-occurring words, or latent topics. A challenging step of topic modeling is determining the number of topics to extract. This tutorial describes tools researchers can use to identify the number and labels of topics in topic modeling. First, we outline the procedure for narrowing down a large range of models to a select number of candidate models. This procedure involves comparing the large set on fit metrics, including exclusivity, residuals, variational lower bound, and semantic coherence. Next, we describe the comparison of a small number of models using project goals as a guide and information about topic representative and solution congruence. Finally, we describe tools for labeling topics, including frequent and exclusive words, key examples, and correlations among topics.

An intrusion detection system using Exponential Henry Gas Solubility Optimization based Deep Neuro Fuzzy Network in MANET

The smoothness and dynamic nature of the exploitation are significantly influenced by Mobile Ad hoc Networks (MANETs). The wireless and active environments, on the other hand, are vulnerable to a range of threats. Enhancing the reliable intrusion detection model is crucial to safeguarding the MANET from various malicious attacks. In this paper, an effective intrusion detection method for MANET is presented based on a Deep Neuro Fuzzy Network based on Exponential-Henry Gas Solubility Optimization (EHGSO). The newly designed EHGSO algorithm is used to select the optimal routes in the early phases of safe routing. The fitness metrics that define this approach include energy, distance, neighbourhood quality, and link quality. The proposed EHGSO combines the Henry Gas Solubility Optimization (HGSO) with Exponential Weighted Moving Average (EWMA). The second phase, in which the conveyed data packets are altered and the Knowledge discovery in databases (KDD) features are extracted, starts the intrusion detection phase at the base station. Following the extraction of the KDD features, data augmentation is performed. The Deep Neuro Fuzzy Network is trained using the suggested EHGSO method before performing intrusion detection. The proposed method demonstrates higher performance when compared to all other existing technologies. In without attacks scenario, the values achieved by the proposed method considering the metrics, such as energy, throughput, packet drop, jitter, Performance and Development Review (PDR), precision, and recall is 0.342 J, 134975 kbps, 4.123, 0.086, 95.877%, 0.950, and 0.924, respectively.

Association of Selected Fitness Variables with Perceived Performance Levels in Lawn Tennis Players

Abstract Objectives Lawn tennis is an explosive sport that involves various fitness parameters such as explosive strength, endurance, power, and agility. Improved physical fitness leads to superior athletic performance. The coach's perception is contingent upon his perception of the player at the time the athlete is comparing himself to the coach. Thus, this study aimed to determine the association between chosen fitness factors and perceived performance by coach in lawn tennis players. Thus, the study aims to detect the relationship between different fitness variables and perceived level of performance in lawn tennis player. Materials and Methods Fifty-two lawn tennis players from two sports academies were measured for various parameters of fitness and perceived performance level as rated by the coach. Statistical Analysis The association between selected fitness metrics and perceived performance in lawn tennis players was investigated using Pearson's correlation test. Furthermore, the data were analyzed with paired t-test for male and female group, and ANOVA was used to analyze the difference between the levels of performance. Results Fatigue index, closed kinetic chain upper extremity stability (CKCUEST) test, and peak power and energy expenditure taken by metabolic equivalent (MET) calculation was found to be statistically different between males and females. While comparing BMI with the peak power, there was a weak positive correlation for males, while for female there was no correlation. Metabolic equivalent, energy expenditure, and peak power were positively correlated with the level of performance; however, fatigue index and level of performance had a negative nonsignificant correlation. Conclusion Peak power, fatigue index, energy expenditure, and BMI were found to be associated in males, but not in females. Peak power and energy expenditure were connected to performance in both genders.

AIROPA IV: Validating point spread function reconstruction on various science cases

We present an analysis of six independent on-sky datasets taken with the Keck-II/NIRC2 instrument. Using the off-axis point spread function (PSF) reconstruction software AIROPA, we extract stellar astrometry, photometry, and other fitting metrics to characterize the performance of this package. We test the effectiveness of AIROPA to reconstruct the PSF across the field of view in varying atmospheric conditions, number and location of PSF reference stars, stellar crowding, and telescope position angle (PA). We compare the astrometric precision and fitting residuals between a static PSF model and a spatially varying PSF model that incorporates instrumental aberrations and atmospheric turbulence during exposures. Most of the fitting residuals we measure show little to no improvement in the variable-PSF mode over the single-PSF mode. For one of the data sets, we find photometric performance is significantly improved (by ∼10 × ) by measuring the trend seen in photometry as a function of off-axis location. For nearly all other metrics we find comparable astrometric and photometric precision across both PSF modes, with a ∼13 % smaller astrometric uncertainty in variable-PSF mode in the best case. We largely confirm that the spatially variable PSF does not significantly improve the astrometric and other PSF fitting residuals over the static PSF for on-sky observations. We attribute this to unaccounted instrumental aberrations that are not characterized through afternoon adaptive optics (AO) bench calibrations.

Event Log Privacy Based on Differential Petri Nets

ABSTRACT Process mining uses event logs to improve business processes, but such logs may contain privacy information. One popular research problem is the privacy protection of event logs. Publishing logs with differential privacy is one of major research directions. Existing research achieves privacy protection primarily by injecting random noise into event logs, or merging similar information. The former ignores the fact that injecting random noise will produce apparently unreasonable activity traces, and the latter will cause a loss of process information in the process mining perspective. To solve the above problems, this article proposes a differential algorithm based on randomized response to model Petri nets for the original event logs, select the important labels in the logs by the weak sequential relationship of control flow between activities, inject noise into the Petri net model based on the important labels using the randomized response approach, and establish a differential Petri net model. Experiments on public datasets show that the event logs produced by the approach proposed do not contain unreasonable traces. Compared with the baseline approach, the proposed approach performs better on Fitness metrics with consistent privacy requirements and retains more process variants, reducing the loss of original event log process information.

Indirect genetic effects for social network structure in Drosophila melanogaster.

The position an individual holds in a social network is dependent on both its direct and indirect social interactions. Because social network position is dependent on the actions and interactions of conspecifics, it is likely that the genotypic composition of individuals within a social group impacts individuals' network positions. However, we know very little about whether social network positions have a genetic basis, and even less about how the genotypic makeup of a social group impacts network positions and structure. With ample evidence indicating that network positions influence various fitness metrics, studying how direct and indirect genetic effects shape network positions is crucial for furthering our understanding of how the social environment can respond to selection and evolve. Using replicate genotypes of Drosophila melanogaster fruit flies, we created social groups that varied in their genotypic makeup. Social groups were videoed, and networks were generated using motion-tracking software. We found that both an individual's own genotype and the genotypes of conspecifics in its social group affect its position within a social network. These findings provide an early example of how indirect genetic effects and social network theory can be linked, and shed new light on how quantitative genetic variation shapes the structure of social groups. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Specification-based Autonomous Driving System Testing

Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be done safely in very realistic and highly customizable environments. Existing testing approaches, however, fail to test simulated AVs systematically, as they focus on specific scenarios and oracles (e.g., lane following scenario with the “no collision” requirement) and lack any coverage criteria measures. In this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathtt {AVUnit}$</tex-math></inline-formula> , a framework for systematically testing AV systems against customizable correctness specifications. Designed modularly to support different simulators, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathtt {AVUnit}$</tex-math></inline-formula> consists of two new languages for specifying dynamic properties of scenes (e.g. changing pedestrian behaviour after waypoints) and fine-grained assertions about the AV's journey. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathtt {AVUnit}$</tex-math></inline-formula> further supports multiple fuzzing algorithms that automatically search for test cases that violate these assertions, using robustness and coverage measures as fitness metrics. We evaluated the implementation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathtt {AVUnit}$</tex-math></inline-formula> for the LGSVL+Apollo simulation environment, finding 19 kinds of issues in Apollo, which indicate that the open-source Apollo does not perform well in complex intersections and lane-changing related scenarios.

Constructing an indices system for evaluating the ecological integrity of forests in western Sichuan, China based on structural equation modeling

With the concern for biodiversity conservation and ecological sustainability, the traditional method of assessing conservation management based on single elements such as timber production, stand structure, and forest area is no longer applicable. Especially for China, where the primary goal of national park conservation management is to “protect and maintain the authenticity and integrity of natural ecosystems”, it is necessary to construct a set of indicators that can comprehensively evaluate the effectiveness of natural ecosystem conservation management. Therefore, in this study, we take Jiuzhaigou and Giant Panda National Park, in Sichuan, China, as examples, and construct a set of ecological integrity evaluation indices system from the perspective of attributes of forest ecosystems. Initially, we screened the candidate indicators based on the coefficient of variation, distribution range, and redundancy analysis and identified 16 indicators as the evaluation indices of ecological integrity. Next, the model fit and the interrelationship of the components of the ecological integrity model were verified using a combination of structural equation modeling and actual data. The model validation results showed that: (1) CMIN/DF = 1.66, RMSEA = 0.058, GFI = 0.954, and other model fit metrics have reached the best reference standard, indicating that the ecological integrity model overall fit is high. (2) In the parameter estimation results of the model observed and latent variables, the ecological integrity model explained 96%, 85% and 89% of the variance in species composition, stand structure and ecological processes, respectively, indicating that the evaluation indices selected can effectively predict the ecological integrity of forests. In addition, we used the factor loadings in the structural equation model to calculate the weights of the indices and used the weighted sum to calculate the ecological integrity. A one-way ANOVA was conducted to compare the differences among the ecological integrity of forests of different origins, forest types and life forms. The relative importance and linear regression were used to elucidate the main factors affecting forest ecological integrity. The results showed that (1) except for the scrub community, the mean value of forest ecological integrity was >0.45, which was in the middle-to-upper level. (2) the ecological integrity of forests of different origins and different forest types showed a changing pattern: natural forest > plantation forest, mixed forest > pure forest. (3) Stand age and elevation strongly influenced the ecological integrity of forests, with relative contribution rates of 64.54% and 11.88%, respectively.

Can recurrent neural networks learn process model structure?

Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely next event or suffix, its remaining time, or an outcome-related variable. Recurrent neural networks (RNNs), and more specifically long short-term memory nets (LSTMs), stand out in terms of popularity. In this work, we investigate the capabilities of such an LSTM to actually learn the underlying process model structure of an event log. We introduce an evaluation framework that combines variant-based resampling and custom metrics for fitness, precision and generalization. We evaluate 4 hypotheses concerning the learning capabilities of LSTMs, the effect of overfitting countermeasures, the level of incompleteness in the training set and the level of parallelism in the underlying process model. We confirm that LSTMs can struggle to learn process model structure, even with simplistic process data and in a very lenient setup. Taking the correct anti-overfitting measures can alleviate the problem. However, these measures did not present themselves to be optimal when selecting hyperparameters purely on predicting accuracy. We also found that decreasing the amount of information seen by the LSTM during training, causes a sharp drop in generalization and precision scores. In our experiments, we could not identify a relationship between the extent of parallelism in the model and the generalization capability, but they do indicate that the process' complexity might have impact.

Polygenic risk score association with cognitive decline in Parkinson’s Disease

AbstractBackgroundCognitive impairment is a common and debilitating symptom in Parkinson’s disease (PD) with high variability in individual trajectory of decline. We sought to explore heterogeneity in the trajectory of individual cognitive change in a cohort of early stage PD patients and test association to cumulative genetic risk identified in large scale Genome Wide Association Studies (GWAS).MethodUsing longitudinal measures of the Montreal Cognitive Assessment (MoCA) we employed latent class mixed modelling (LCMM) to identify and investigate unknown populations in the Parkinson’s Progression Markers Inititative (PPMI) de‐novo PD cohort. Tranformed MoCA scores were modelled as a quadratic function of years from baseline, controlling for age, gender and motor symptom severity. Optimal group number was identified and determined using standardly advised model fit metrics. Polygenic risk scores (PRS) for five GWAS were calculated using PRSice‐2 applied to genotyping array data. Association of PRS with cognitive groups was tested using linear models and ANOVA tests.ResultLCMM showed optimal fit statistics for three classes (lowest BIC, high entropy) and these groups were retained for further analysis The largest identified class (n = 240) on average, presented at baseline with higher MoCA scores and remained stable over time. The second class (n = 132) presented with lower MoCA scores and showed a slow declining trajectory whilst the smallest class (n = 13) presented with lower MoCA scores but declined at a rapid rate. Educational attainment and Alzheimer’s disease (AD) GWAS derived PRS were significantly associated with cognitive class and explained the highest amount of phenotypic variance. For PD case‐control status, only the PD PRS was significantly associated with Parkinson’s status and explained a similar level of phenotypic variation.ConclusionLatent class analysis may provide utility in subsetting longitudinal cognitive outcome groups for use in groupwise comparisons. Using this method we show evidence for association of educational attainment and AD cumulative genetic risk and worse cognitive outcomes in early PD.

Risk Factors for Cardiometabolic Disease in Professional Firefighters.

Firefighters are plagued with cardiometabolic disease (CMD). Obesity, poor cardiorespiratory and muscular fitness, and blood lipids (low-density lipoprotein cholesterol, triglycerides, low high-density lipoprotein cholesterol) are risk factors for CMD. However, markers of oxidative stress, inflammation, and insulin resistance can provide further insight regarding CMD risk. This study investigated the relationships between fitness metrics (cardiorespiratory and muscular fitness, percent body fat, waist circumference), blood lipids, blood pressure, and years of experience as a firefighter to blood markers of insulin resistance: Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), oxidative stress: advanced oxidation protein products (AOPPs), and inflammation: C-reactive protein. Waist circumference and blood concentrations of triglycerides were significantly related to AOPPs and HOMA-IR. Cardiorespiratory fitness was inversely related to AOPPs, HOMA-IR and C-reactive protein. These findings demonstrate the importance of high cardiorespiratory fitness and low waist circumference to reduce markers of CMD.

Sustainable Multipath Routing for Improving Cross-Layer Performance in MANET Using an Energy Centric Tunicate Swarm Algorithm

Generally, battery power is a valuable resource for mobile devices in a Mobile Ad Hoc Network (MANET). Therefore, energy efficiency and network lifetime should be taken into account when developing control strategies. However, designing an energy-efficient routing mechanism necessitates consideration of many nodes from many layers, such as remaining energy, overall traffic load, and channel assumptions. The traditional layered strategy is unsuccessful in dealing with power-related issues that might affect all layers of the stack. In this paper, the Energy Centric Tunicate Swarm Algorithm (ECTSA) is proposed to perform the cross-layer routing over MANET. The fitness metrics considered in the ECTSA to improve the cross-layer routing are residual energy, communication cost, Data Success Rate (DSR), and mobility. Additionally, an Adaptive Competition Window (ACW) adjustment is used for minimizing the energy consumption caused by the contentions. The performance of the proposed ECTSA is analyzed by means of energy consumption, Packet Delivery Ratio (PDR), End-to-End Delay (EED), and routing overhead. Next, the existing techniques, namely, CEELBRP and EECRP-PSO, are used to evaluate the efficiency of the ECTSA method. The energy consumption of the ECTSA is 7.1 joules and prolongs the network lifetime up to 1603 s for 50 nodes, which is better when compared to the existing CEELBRP and EECRP-PSO techniques.

Virtual cardiac fitness training in pediatric heart transplant patients: A pilot study.

Cardiac fitness training in pediatric heart transplant recipients can improve functional capacity. Widespread implementation has been limited mostly due to logistical constraints, specifically related to travel. The aim of this study was to implement and assess a virtual cardiac fitness program for pediatric heart transplant patients. Participants were between the age of 10 and 20 years old. All subjects completed an initial 6MWT, strength/flexibility assessment, and QOL assessment with the PROMIS measurement. Participants then underwent a 16-week intervention with exercise sessions twice weekly for 30 min with a trained exercise physiologist over a virtual platform. At the end of the intervention period, participants repeated a 6MWT, strength/flexibility assessment, and PROMIS measurement. Throughout the study, patients wore a FitBit accelerometer to monitor daily activity levels. Thirteen individuals were enrolled. Mean age was 15.4 years (SD =3.4) with a mean post-transplant period of 9.7 years (SD = 4.3). Session attendance was 83%. Post-intervention measurements showed improvements in 6MWT (median, +21 m, p=.02), push-up repetitions (median, +5 rep, p=.0005), wall-sit duration (median, +10 s, p=.001), plank duration (median, +9 s, p=.03), sit-up repetitions (median, +7 rep, p=.002), and sit and reach distance (median, +5 cm, p=.04). PROMIS measurement showed significant improvements in self-reported fatigue (Δz-score, -7.7, p=.008) and sleep impairment (Δz-score, -5.9, p=.002). Average daily step count increased 1464 steps per day per patient (p=.008). We have demonstrated the successful implementation of a virtual cardiac fitness with excellent adherence and improvement in physical fitness and QOL metrics.

Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging

From the perspective of FDR (fault detection rate), which is an indispensable component in reliability modeling, this paper proposes two kinds of reliability models under imperfect debugging. This model is a relatively flexible and unified software reliability growth model. First, this paper examines the incomplete phenomenon of debugging and fault repair and established a unified imperfect debugging framework model related to FDR, which is called imperfect debugging type I. Furthermore, it considers the introduction of new faults during debugging and establishes a unified imperfect debugging framework model that supports multiple FDRs, called imperfect debugging type II. Finally, a series of specific reliability models are derived by integrating multiple specific FDRs into two types of imperfect debugging framework models. Based on the analysis of the two kinds of imperfect debugging models on multiple public failure data sets, and the analysis of model performance differences from the perspective of fitting metrics and prediction research, a fault detection rate function that can better describe the fault detection process is found. By incorporating this fault detection rate function into the two types of imperfect debugging models, a more accurate model is obtained, which not only has excellent performance and is superior to other models but also describes the real testing process more accurately and will guide software testers to quantitatively improve software reliability.

Deep learning model for predicting tunnel damages and track serviceability under seismic environment.

Jammu and Kashmir in the northwestern part of the Himalayan region is frequently triggered with moderate to large magnitude earthquakes due to an active tectonic regime. In this study, a mathematical formulation-based Seismic Tunnel Damage Prediction (STDP) model is proposed using the deep learning (DL) approach. The pertinency of the DL model is validated using tunnel damage data from historical earthquakes such as the 1999 Chi-Chi earthquake, the 2004 Mid-Niigata earthquake, and the 2008 Wenchuan earthquake. Peak ground acceleration (PGA), source to site distance (SSD), overburden depth (OD), lining thickness (t), tunnel diameter (Ф), and geological strength index (GSI) were employed as inputs to train the Feedforward Neural Network (FNN) for damage state prediction. The performance evaluation results provided a clear indication for further use in a variety of risk assessment domains. When compared to models based on historical data, the proposed STDP model produces consistent results, demonstrating the robustness of the methodology used in this work. All models perform well during validation based on fitness metrics. The “STD multiple graphs” is also proposed which provide information on damage indexing, damage pattern, and crack predictive specifications. This can be used as a ready toolbox to check the vulnerability in post-seismic scenarios. The seismic design guidelines for tunnelling projects are also proposed, which discuss the damage pattern and suggest mitigation measures. The proposed STDP model, STD multiple graphs, and seismic design guidance are applicable to any earthquake-prone tunnelling project anywhere in the world.