15 research outputs found
Machine learning prediction of the unconfined compressive strength of controlled low strength material using fly ash and pond ash
No full textAbstract The sustainable use of industrial byproducts in civil engineering is a global priority, especially in reducing the environmental impact of waste materials. Among these, coal ash from thermal power plants poses a significant challenge due to its high production volume and potential for environmental pollution. This study explores the use of controlled low-strength material (CLSM), a flowable fill made from coal ash, cement, aggregates, water, and admixtures, as a solution for large-scale coal ash utilization. CLSM is suitable for both structural and geotechnical applications, balancing waste management with resource conservation. This research focuses on two key CLSM properties: flowability and unconfined compressive strength (UCS) at 28 days. Traditional testing methods are resource-intensive, and empirical models often fail to accurately predict UCS due to complex nonlinear relationships among variables. To address these limitations, four machine learning models—minimax probability machine regression (MPMR), multivariate adaptive regression splines (MARS), the group method of data handling (GMDH), and functional networks (FN) were employed to predict UCS. The MARS model performed best, achieving R2 values of 0.9642 in training and 0.9439 in testing, with the lowest comprehensive measure (COM) value of 1.296. Sensitivity analysis revealed that cement content was the most significant factor with obtaining R = 0.88, followed by water (R = 0.82), flowability (R = 0.79), pond ash (R = 0.78), curing period (R = 0.73), and fine content (R = 0.68), with fly ash (R = 0.55) having the least impact. These machine learning models provide superior accuracy compared to traditional methods, particularly in handling complex interactions between mix components. The proposed models offer a practical approach for predicting CLSM performance, supporting sustainable construction practices and the efficient use of industrial byproducts. The novelty of this study lies in the development of precise design equations for evaluating UCS, promoting both practical applicability and environmental sustainability
Development of fragility curve for railway embankment
No full textFor the construction of railway embankments, geotechnical engineers pay special attention to slope stability studies. The factor of safety values plays a crucial part in assessing the safe design of slopes. The factor of safety values is used to determine how close or far slopes are from failing due to natural or man-made causes. The factor of safety is a numeric value to indicate the relative stability, it doesn’t tell about the actual risk level of any structure, but the reliability index and probability of failure quantify the risk level. The present study discusses the findings of a study to determine the factor of safety of an embankment of height 12.3 m by using Geo-studio 2012 software. In this article, the fragility curve for six different types of cross-sections was also developed i.e. the graph between the probability of failure ( ) and horizontal seismic coefficient ( ), for various values of (i.e. 0.1, 0.12, 0.144, 0.18, 0.2, 0.3, 0.4 and 0.5). It is observed from the developed fragility curve, as the value increases value decreases. A fragility curve can be used to calculate failure probability over a range of seismic zones, and for design purposes, a given seismic zone and probability of failure a unique reliable side slope is selected. Further, two machine learning (ML) models namely, Deep Neural Network (DNN) and Support Vector Regression (SVR) have been developed for the prediction of the factor of safety for different sides slope. Obtained correlation values (R) for SVR and DNN are approximately 0.95 and 0.82 respectively. From the help of the predicted factor of safety fragility curve against horizontal seismic coefficient is drawn for both SVR and DNN models, that for reducing the time of calculation and ease in working best result giving model will be suggested for further analysis of railway embankment
Improved classical and quantum algorithms for the shortest vector problem via bounded distance decoding
No full textThe most important computational problem on lattices is the shortest vector problem (SVP). In this paper, we present new algorithms that improve the state-of-the-art for provable classical/quantum algorithms for SVP. We present the following results: (1) A new algorithm for SVP that provides a smooth tradeoff between time complexity and memory requirement. For any positive integer 4 ≤ q ≤ √n, our algorithm takes q13n+o(n) time and requires poly(n) ̇ q16n/q2 memory. This tradeoff, which ranges from enumeration (q = √n) to sieving (q constant), is a consequence of a new time-memory tradeoff for discrete Gaussian sampling above the smoothing parameter. (2) A quantum algorithm for SVP that runs in time 20.950n+o(n) and requires 20.5n+o(n) classical memory and poly(n) qubits. In a quantum random access memory (QRAM) model, this algorithm takes only 20.835n+o(n) time and requires a QRAM of size 20.293n+o(n), poly(n) qubits and 20.5n classical space. This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [D. Aggarwal et al., Solving the shortest vector problem in 2n time using discrete Gaussian sampling: Extended abstract, in Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing (STOC), 2015, pp. 733-742] that has a time and space complexity 2n+o(n). (3) A classical algorithm for SVP that runs in time 21.669n+o(n) time and 20.5n+o(n) space. This improves over an algorithm of [Y. Chen, K. Chung, and C. Lai, Quantum Inf. Comput., 18 (2018), pp. 285-306] that has the same space complexity. The time complexity of our classical and quantum algorithms are obtained using a known upper bound on a quantity related to the lattice kissing number, which is 20.402n. We conjecture that for most lattices this quantity is a 2o(n). Assuming that this is the case, our classical algorithm runs in time 21.292n+o(n), our quantum algorithm runs in time 20.750n+o(n), and our quantum algorithm in a QRAM model runs in time 20.667n+o(n). As a direct application of our result, using the reduction in [L. Ducas, Des. Codes. Cryptogr., 92 (2024), pp. 909-916], we obtain a provable quantum algorithm for the lattice isomorphism problem in the case of the trivial lattice \BbbZn (\BbbZLIP) that runs in time 20.417n+o(n). Our algorithm requires a QRAM of size 20.147n+o(n), poly(n) qubits and 20.25n classical space
Predicting the UCS of Industrial Byproduct-Based CLSM Using Machine Learning and Experiments
No full textThis study investigated the development of sustainable Controlled Low Strength Material (CLSM) using industrial by-products pond ash, fly ash, and red mud as alternatives to conventional concrete constituents. This research employs a dual methodology: comprehensive experimental testing aligned with ASTM standards and the implementation of advanced machine learning (ML) techniques to predict the unconfined compressive strength (UCS) of CLSM mixes. Experimental datasets, generated through the variation of key material and mix design parameters, were utilized to train ensemble-based supervised ML models, including ADAboost, XGBoost, gradient boosting machine (GBM), and random forest (RF). A comparative performance evaluation was conducted, and the XGBoost model emerged as the most accurate predictor, achieving R² values of 0.969 for training and 0.933 for testing, surpassing GBM, ADAboost, and RF across multiple performance indicators. The optimal model was subsequently embedded into a graphical user interface (GUI) for UCS prediction. A sensitivity analysis based on the XGBoost model revealed that cement, water, and curing age were the most influential parameters affecting UCS, with cement exhibiting the highest impact value of 0.86 and a relative contribution of 19%. These findings emphasize the significance of these variables in strength development and mix optimization. The integration of experimental validation with predictive modeling not only advances the understanding of CLSM behavior but also underscores the utility of ML in the formulation of sustainable construction materials. This research supports the beneficial reuse of industrial waste, aligns with environmental sustainability goals, and provides an efficient and reliable tool for CLSM mix design
Normal range of motion of hip and ankle in Indian population
No full textAbstract
Objective: Most studies that determine the range of motion of joints of the lower limbs study the Western population. The Asian population differs significantly, as daily activities demand different sitting positions. Our study aimed to establish the normal values of hip and ankle range of motion in various age groups in the Indian population and the effect of various functional positions of the hip on range of motion. \r\nMethods: Three hundred and twenty-six Indian subjects, between the ages of 1 month to 75 years, were randomly selected for measurement of the range of motion of the hip and ankle joint. Exclusion criteria included history of injury or disease related to the lower extremities. Changes with age in the arc of joint motion were studied. The influence of various functional positions of the lower limb on the range of motion of the hip and the effect of weight-bearing on the ankle joint range of motion were also analyzed. \r\nResults: Hip range of motion differed in various positions. Hip rotations were significantly greater when measured with the knee in flexion in both the sitting and prone positions than in the supine position. The arc of hip rotation was highest in the prone position. A significant increase in the arc of ankle dorsiflexion was found in a weight-bearing (squatting) position. Age related reduction in movement was found mainly in the rotations of the hip and dorsiflexion of the ankle. \r\nConclusion: The data compiled in this study on the range of motion in the hip and ankle joint of the Indian population will be useful in the evaluation of patients with disorders of these joints, especially in the Indian and Asian population.
Özet
Amaç: Alt ekstremitedeki eklemlerin hareket açıklığını inceleyen çalışmaların çoğu Batılı toplumları ele almıştır. Günlük aktiviteler değişik oturma pozisyonlarını gerektirdiğinden, Asya toplumlarının değerlendirilmesi önemli ölçüde farklıdır. Çalışmamızın amacı, Hint toplumunda farklı yaş gruplarındaki normal kalça ve ayak bileği hareket açıklıklarını ve kalçanın çeşitli fonksiyonel hareketlerinin hareket açıklığına etkilerini belirlemekti. \r\nÇalışma planı: Kalça ve ayak bileği eklemlerinin hareket açıklığının ölçülmesi için yaşları 1 ay ile 75 yıl arasında değişen 326 Hint olgu rastgele seçildi. Alt ekstremitelerinde yaralanma veya hastalık öyküsü olanlar çalışmaya alınmadı. Eklem hareketi arkında yaşa bağlı değişimler incelendi. Alt ekstremitenin çeşitli fonksiyonel pozisyonlarının hareket açıklığına etkisi ve yük vermenin ayak bileği ekleminin hareket açıklığına etkisi de analiz edildi. \r\nBulgular: Kalça hareket açıklığı değişik pozisyonlarda farklılıklar gösterdi. Hem oturur hem yüzükoyun pozisyonda ve diz fleksiyonda iken ölçülen kalça rotasyonu, supin pozisyonda ölçülen rotasyondan anlamlı derecede daha yüksekti. Kalça rotasyon arkı yüzükoyun pozisyonda en üst seviyede idi. Çömelerek yük verme pozisyonunda ayak bileği dorsifleksiyon arkında belirgin bir artış görüldü. Hareketlerde yaşa bağlı azalma çoğunlukla kalça rotasyonunda ve ayak bileği dorsifleksiyonunda gözlendi. \r\nÇıkarımlar: Bu çalışmada elde edilen, Hint toplumunda kalça ve ayak bileği hareket açıklığına dair veriler, özellikle Hint ve Asya toplumlarının bu eklemlerde karşılaşacağı sorunların değerlendirilmesinde faydalı olacaktır
Estimation of the compressive strength of ultrahigh performance concrete using machine learning models
No full textThe compressive strength of ultrahigh performance concrete (UHPC) is influenced by the composition, quality, and quantity of its constituent elements. Using traditional statistical methods, it is difficult for us to quantify the relationships between the technical properties of UHPC and the composition of the mixture because of their complexity and nonlinearity. This work aims to develop advanced prediction models for estimating UHPC compressive strength over a large spectrum of supplementary cementitious material combinations and aggregate sizes. The models trained on the UHPC mixture dataset with 15 input variables included the group method of data handling, recurrent neural networks, long short-term memory, and bidirectional long short-term memory (Bi-LSTM). These models routinely forecast UHPC compressive strength according to sensitivity analysis, external validation, and statistical performance measures. During testing, the Bi-LSTM model outperformed the other models, with an RMSE of 0.0482 and an R² value of 0.9464. These results maximize component selection by showing how effectively the Bi-LSTM model might reduce UHPC formulation development and lower the cost and testing time span
Soft computing-based prediction models for compressive strength of concrete
No full textThe complexity of concrete's composition makes it difficult to predict its compressive strength, which is a highly valuable and desired characteristic. Traditional methods for prediction are expensive and time-consuming, resulting in limited data availability. However, modern soft-computing models have emerged as a reliable solution for accurately forecasting concrete's compressive strength. The research proposes a novel Deep Neural Network (DNN), Multivariate Adaptive Regression Splines (MARS) and Extreme Learning Machine (ELM) based machine learning (ML) models for forecasting the compressive strength of concrete added with various proportions of fly ash and silica fume. For this purpose, a dataset of 144 trials, having 8 input parameters is taken from the literature. The performance of the models is confirmed using various statistical parameters. Rank Analysis reveals that DNN is the best-performing model (Rank =52, RTR2 =0.983 and RTs2 =0.954), closely followed by MARS (Rank =38, RTR2 =0.974 and RTs2 =0.956); while ELM lags behind the other two counterparts. The results are further confirmed using an error matrix, external validation and AIC criteria. The visual interpretation is provided using the Taylor diagram. MARS has the edge over the other two models in terms of providing a user-friendly solution
Optimization of Pond-Ash-Based Controlled Low-Strength Materials with Lime and Superplasticizer via Experiments and Supervised Machine Learning
No full textThe growing production of industrial byproducts such as pond ash and fly ash from thermal power plants presents a major waste management challenge. Integrating these byproducts into controlled low-strength materials (CLSM) offers a sustainable solution for backfilling behind retaining walls, tunnels, and utility trenches. For applications requiring higher strength, CLSM mixtures must achieve compressive strengths above 0.7 MPa after 28 days. This study investigates the effects of adding superplasticizers and lime to conventional CLSM materials through experimental work to develop high-strength CLSM mixtures. Results show a significant improvement in compressive strength with these additives, a finding not previously reported. To complement the experiments, machine learning models were developed to predict the unconfined compressive strength (UCS) of CLSM based on varying proportions of cement, lime, superplasticizers (SP), and pond ash as traditional experimental and empirical approaches are limited in capturing nonlinear interactions among mix parameters. A comprehensive dataset was created from systematic variations in mix proportions and corresponding strength measurements. Four predictive models XGBoost, XGBoost-GWO, XGBoost-PSO, and XGBoost-SSO were trained and tested. The XGBoost-SSO model achieved the best performance with R² values of 0.990 (training), 0.979 (validation), and 0.974 (testing), along with the lowest RMSE (0.026 MPa) and MAE (0.019 MPa) in the testing phase. Regression and REC analyses confirmed its superior predictive capability. Sensitivity analysis identified pond ash (55%) and cement (17.6%) as the most influential factors. A user-friendly GUI tool was also developed for real-time UCS prediction and data-driven mix optimization
Slide reduction, revisited—filling the gaps in svp approximation
No full textInternational audienceWe show how to generalize Gama and Nguyen's slide reduction algorithm [STOC '08] for solving the approximate Shortest Vector Problem over lattices (SVP) to allow for arbitrary block sizes, rather than just block sizes that divide the rank n of the lattice. This leads to significantly better running times for most approximation factors. We accomplish this by combining slide reduction with the DBKZ algorithm of Micciancio and Walter [Eurocrypt '16]. We also show a different algorithm that works when the block size is quite large-at least half the total rank. This yields the first non-trivial algorithm for sublinear approximation factors. Together with some additional optimizations, these results yield significantly faster provably correct algorithms for δ-approximate SVP for all approximation factors n 1/2+ε ≤ δ ≤ n O(1) , which is the regime most relevant for cryptography. For the specific values of δ = n 1−ε and δ = n 2−ε , we improve the exponent in the running time by a factor of 2 and a factor of 1.5 respectively
Modelling shocks using molecular dynamics
No full textThe study of shocks in solid, crystalline metals has been ongoing since
the early works of Rankine and Hugoniot in the latter half of the 19th
century. However, the understanding of the behaviour of such materials
under these extreme conditions remains an area of active research because
of the paucity with which models can predict experimental observations.
The modern era has seen a huge increase in the ability to solve many of
the problems of this area of study by numerical, rather thatn analytic,
means. One of these tools has been the use of computers to provide a
numerical solution to the many–body problem posed by consideration of
the medium as being composed of interacting atoms.
The issue, then, has been transferred from one of dealing with many particles
(which remains a problem for some aspects) to one of being able to
develop a model which correctly describes the atomic interactions. However,
it has been found that approximately correct models provide sufficient
fidelity to enable qualitative studies to be undertaken.
The study undertaken here has used this advantage to consider the behaviour
of metallic materials under weak shock conditions. A comparison
with some previous studies is given, which shows that, in order to avoid
certain behaviours not observed experimentally, the simulation must contain
thermal motion equivalent to at least room temperature. This thermal
motion, and its resultant misalignment of the atoms, prevents spurious
transfer of uni-directional momentum into rebounding translational
supersonic waves. Further examination of the initial generation of dislocations
indicates differences in the behaviour of not only the three high
symmetry directions, but in the way that shear stress is relieved initially in
low symmetry crystals as well. This behaviour gives some indication as to
how the elastic precursor, commonly observed in weak shock experiments,
decays from the level predicted by the Rankine–Hugoniot conservation relations
to the much lower level observed experimentally. However, a very
large discrepancy exists between the amplitude of the elastic wave observed
in these simulations and that of experiments. It is shown that the
existence of defects within the crystal can account for at least some of this
discrepancy. However, computational limitations not only prevent the creation
of realistic sample sizes, but also prevent the simulation of realistic
defect densities and microstructures. This computational limtation, then,
means that it is not currently possible to recreate the low Hugoniot elastic
limits observed experimentally.
The inability of atomistic simulations to recreate experimental data notwithstanding,
useful analysis of shock behaviour is demonstrated. This fortuity
is used to examine the behaviour of bicrystals under shock loading. It
is shown that the difference in shock speed, together with the difference
in response of the two crystal orientations leads to an interaction which
modifies the behaviour from that observed in single crystal simulations.
Further use is made of the ability of modern simulation methods to recreate
salient features of dynamic processes to examine the behaviour of
metallic substrates under high–speed impact from nanometer sized particles.
Here the plasticity of the substrate is shown to be vital to ensuring
that the simulation results are faithful to experiment, and hence to space
science work. In order to capture this behavioour correctly, issues of substrate
size and boundary behaviour are seen to be key
