Applications of Artificial Intelligence in Construction Industry: A Review

Construction is probably the most seasoned calling as individuals have been building safe houses and structures for centuries. In any case, the business has advanced a lot in the manner they configuration, plan, and assemble structures. As of late, development organizations have progressively begun utilizing AI in a scope of approaches to make development more effective and imaginative. From advancing work routines to improving work environment wellbeing to keeping a protected watch on development offices, AI in the development business is as of now demonstrating its worth. Development supervisors have been discovering an incentive with AI and psychological innovations to help mechanize a significant number of the everyday except fundamental assignments to running their tasks. They are discovering AI accommodating with booking related assignments so as to forestall postponements, clashes, and different issues. This is both on the staff level of planning and on the undertaking and materials side. For little scope ventures people may have the option to oversee entangled development calendars and procedures physically. Nonetheless, enormous scope, multi-year ventures require the coordination of many convoluted errands and moving parts, for example, plans and outlines, licenses, and unforeseen postponements and changes that rapidly gain out of power for people to oversee without the help of innovation. The AI can screen hardware, devices and supplies and convey cautions in the event that anybody endeavours to take something from the site. In view of the mind-boggling results AI has conveyed, it's nothing unexpected that the development business is receiving different AI advancements. The advantages that AI can give are still moderately early. In the coming years AI will keep on driving cost reserve funds, time investment funds, and generally enhancements and efficiencies to the development business

Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks

The objective of the research is to estimate the value of the California bearing ratio (CBR) through the application of ANN. The methodology consists of creating a database with soil index and CBR variables of the subgrades and granular base of pavements in Jaen, Peru, carried out in the soil mechanics laboratories of the city and the National University of Jaen. In addition, the Python library Seaborn is for variable selection and relevance, and the scikit-learn and Keras libraries were used for the learning, training, and validation stage. Five ANN are proposed to estimate the CBR value, obtaining an error of 4.47% in the validation stage. It can be concluded that this method is effective and valid to determine the CBR value in subgrades and granular bases of any pavement for its evaluation or design

GEP prediction of scour around a side weir in curved channel

Side-weirs have been widely used in hydraulic and environmental engineering applications. Side-weir is known as a lateral intake structure, which are significant parts of the distribution channel in irrigation, land drainage, and urban sewerage system, by flow diversion device. Local scour involves the removal of material around piers, abutments, side-weir, spurs, and embankments. Clearwater scour depth based on five dimensional parameters: approach flow velocity (V1/Vc), water head ratio (h1–p)/h1, side-weir length (L/r), side-weir crest height (b/p) and angle of bend θ. The aim of this study is to develop a new formulation for prediction of clear-water scour of side-weir intersection along curved channel using Gene Expression Programming (GEP) which is an algorithm based on genetic algorithms (GA) and genetic programming (GP). In addition, the explicit formulations of the developed GEP models are presented. Also equations are obtained using multiple linear regressions (MLR) and multiple nonlinear regressions (MNRL). The performance of GEP is found more influential than multiple linear regression equation for predicting the clearwater scour depth at side-weir intersection along curved channel. Multiple nonlinear regression equation was quite close to GEP, which serve much simpler model with explicit formulation. First published online: 17 Mar 201

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique

Effet de la granulométrie sur le comportement géotechnique de roches stériles concassées utilisées comme surface de roulement sur des routes minières

Résumé Les opérations minières produisent de grandes quantités de roches stériles qui sont généralement entreposées dans des haldes à stériles. La gestion et la restauration de ces empilements sont complexes en raison de leur taille (h > 100 m, A > 100 ha) et de leur stabilité géotechnique et géochimique. La valorisation (i.e. réutilisation) des roches stériles permet de réduire l’empreinte des haldes tout en répondant aux besoins de matériaux de construction pour les infrastructures minières. Les stériles sont couramment utilisés pour la construction des routes sur les sites miniers, mais avec peu ou pas de préparation ou de tri particulier. Il en résulte des routes avec une faible durabilité qui contribuent à l’augmentation des crevaisons et à la génération de poussières. L’utilisation dans la surface de roulement de roches stériles concassées dont les propriétés sont optimisées pourrait permettre de réduire l’apparition de défectuosités sur la route et permettre une conduite plus confortable et sécuritaire pour les usagers.L’objectif principal de ce projet de maîtrise était de déterminer les propriétés géotechniques optimales pour l’utilisation des roches stériles concassées comme surface de roulement dans les routes minières. Dans un premier temps, une caractérisation (analyses granulométriques, densité relative des grains solides, compaction Proctor standard et modifié, perméabilité) a été réalisée sur des stériles concassés provenant d’une mine partenaire de l’IRME (Mine Canadian Malartic). La rigidité est un indice important de la performance du matériau utilisé dans la surface de roulement. L’essai de l’indice de portance californien (California bearing ratio test ou CBR) a été utilisé comme indicateur de la rigidité du matériau en fonction de différentes distributions granulométriques, énergies de compaction et teneurs en eau. Des mesures de densités ont été réalisées sur le terrain afin de confirmer si la compaction étudiée au laboratoire correspond à celle observée en place. Les résultats issus du programme de laboratoire ont ensuite été utilisés dans le logiciel SIGMA/W (Geo-Slope Intl. 2018) afin d’évaluer les déformations induites par le passage des véhicules extra-lourds selon les différentes caractéristiques de la surface de roulement. Les résultats des simulations numériques ont permis d’estimer la résistance au roulement correspondant aux différents matériaux modélisés. Les résultats obtenus au laboratoire ont permis de proposer quelques recommandations pour maximiser la rigidité et la densité du matériau destiné à la construction de la surface de roulement de route minière, notamment en modifiant sa distribution granulométrique et l’énergie de compaction employée. Pour le matériau testé, 10% de particules fines (d<0,08 mm) et une énergie de compaction élevée ont permis d’obtenir des CBR supérieurs à 250%. Les résultats ont également montré que les particules les plus grossières du matériau contribuaient significativement à sa rigidité. Les travaux de terrain ont en outre permis de vérifier que l’intervalle des densités mesurées au laboratoire correspondait bien à ce qui est observé sur le terrain. Une certaine hétérogénéité de la distribution des densités et de la distribution granulométrique des roches stériles concassées utilisées dans la surface de roulement a été observée. Finalement, les simulations numériques ont montré que la résistance au roulement pouvait être réduite d’au plus 0,26% grâce à l’augmentation de la rigidité du matériau étudié pour des valeurs de résistance au roulement initiale d’environ 3%.Les résultats de cette étude ont montré que l’optimisation du choix et de la préparation des roches stériles concassées utilisées dans la construction de la surface de roulement des routes minières pouvait améliorer les performances du matériau et ainsi favoriser sa valorisation. ----------Abstract Mining operations generate large volume of waste rocks, which are usually disposed of in piles. Management and reclamation of waste rock piles can be a challenge because of their size (h > 100 m and A > 100 ha) and their geochemical and geotechnical stability. The valorization (i.e. reuse) of these materials reduces their footprint while meeting the needs of construction materials for mine infrastructures. Waste rock is commonly used for road construction at mine sites. However, current practice usually consists of using waste rock directly, without any particular preparation or selection, thus resulting frequently in punctures, dust generation and low durability. Optimizing properties of crushed waste rocks used in the surface course of mine haul roads could reduce the occurrence of road defects and provide a more comfortable and safer ride for users. The main objective of this research project was to determine the optimal geotechnical properties for the use of crushed rock as a running surface in mining roads. First, a characterization (particle size distribution analysis, specific density, standard and modified Proctor compaction, permeability) was performed on crushed waste rock from a partner mine of RIME (Canadian Malartic Mine). Stiffness is an important index of the performance of the material used in the surface course. The California bearing ratio test (CBR) was used as an indicator of the stiffness of the material for different particle size distributions, compaction energies and water contents. Densities were measured in the field to confirm whether the compaction studied in the laboratory corresponded to what is observed in situ. The results from the laboratory program were then used in the SIGMA/W (Geo-Slope Intl. 2018) numerical code to evaluate the deformations induced by the traffic of extra-heavy vehicles according to the different characteristics of the running surface. The rolling resistance corresponding to the different rolling surface materials modelled was evaluated using numerical simulations. Laboratory results showed that it was possible to maximize the stiffness and density the mining road surface by modifying the particle size distribution and the compaction energy used for its construction. For the tested material, 10% fine particle content (d <0.08 mm) and a high compaction energy yielded CBR results greater than 250%. The results also showed that the coarser particles of the material contributed significantly to increase its rigidity. Fieldwork has shown that the range of density measured in the laboratory corresponded well to field conditions. Heterogeneity of density distribution and particle size distribution of the crushed sterile rocks used in the surface course was observed in the field. Finally, the numerical simulations have shown that the increase of the stiffness of the studied material could contribute to decreasing the rolling resistance by a maximum of 0.26% for initial rolling resistance of around 3%. The results of this study showed that the optimization of the crushed waste rocks used in the construction of the surface course of mining roads could improve the performance of the material and thus promote its valorization

Incorporación de agua de mar (NaCl) y concha de molusco (CaCO3) en estabilización de subrasante, Av.2 Promuvi XII, Ilo 2022

La presente investigación tuvo como objetivo evaluar la incidencia del agua de mar (NaCl) y Concha de molusco (CaCO3) en la estabilización de la subrasante, Av.2 Promuvi XII, Ilo 2022. La investigación fue tipo aplicada, enfoque cuantitativo y diseño cuasi experimental; donde se realizó el estudio de población en la AV.2 Promuvi XII teniendo como muestras las 4 calicatas realizadas de un muestreo no probabilístico. Los principales resultados de los ensayos de laboratorio determinaron la clasificación del suelo patrón siendo un suelo A-3 de arena fina según la clasificación AASHTO, donde 5% de NaCl y 20% de CaCO3 fue la dosificación óptima, mejorando las propiedades mecánicas aumentando la máxima densidad seca de 1.913 a 1.953 g/cm3 con NaCl y 1.989 con CaCO3 y reduciendo el óptimo contenido de humedad de 11.30% a 9.30% con NaCl y 9.10% con CaCO3; a su vez mejoró la capacidad de soporte al 95% de compactación de CBR, de 3.80% del suelo patrón, al adicionar 5% NaCl aumentó a 12.82% y con adición de 20% de CaCO3 el resultado fue 38.90%. De esta forma se concluye que la proporción más eficaz para mejorar y estabilizar un suelo arenoso con NaCl es 5% y con CaCO3 es 20%

Prediction of the Effectiveness of Rolling Dynamic Compaction Using Artificial Intelligence Techniques and In Situ Soil Test Data

The research presented in this thesis focuses on developing predictive tools to forecast the effectiveness of rolling dynamic compaction (RDC) in different ground conditions. Among many other soil compaction methods, RDC is a widespread technique, which involves impacting the ground with a heavy (6–12 tonnes) non-circular (3-, 4- and 5- sided) module. It provides the construction industry with an improved ground compaction capability, especially with respect to a greater influence depth and a higher speed of compaction, resulting in increased productivity when compared with traditional compaction equipment. However, to date, no rational means are available for obtaining a priori estimation of the degree of densification or the extent of the influence depth by RDC in different ground conditions. In addressing this knowledge gap, the research presented in this thesis develops robust predictive models to forecast the performance of RDC by means of the artificial intelligence (AI) techniques in the form of artificial neural networks (ANNs) and linear genetic programming (LGP), which have already been proven to be successful in a wide variety of forecasting applications in geotechnical engineering aspects. This study is focussed solely on the 4-sided, 8 tonne impact roller (BH-1300) and the AI-based models incorporate comprehensive databases consisting of in situ soil test data; specifically cone penetration test (CPT) and dynamic cone penetration (DCP) test data obtained from many ground improvement projects involving RDC. Thus, altogether, two distinct sets of optimal models: two involving ANNs – one for the CPT and the other for the DCP; and two LGP models – again, one for the CPT and the other for the DCP – are presented. The accuracy and the reliability of the optimal model predictions are assessed by subjecting them to various performance measures. Furthermore, each of the selected optimal models are examined in a parametric study, by which the generalisation ability and the robustness of the models are confirmed. In addition, the performance of the optimal ANN and LGP-based models, as well as other aspects, are compared with each other in order to assess the suitability and shortcomings of each. Consequently, a recommendation has been made of the most feasible approach for predicting the effectiveness of RDC in different ground conditions with respect to CPT and DCP test data. The models have also been disseminated via a series of mathematical formulae and/or programming code to facilitate their application in practice. It is demonstrated that the developed optimal models are accurate and reliable over a range of soil types, and thus, have been recommended with confidence. As such, the developed models provide preliminary estimates of the density improvement in the ground based on the subsurface conditions and the number of roller passes. Therefore, it is considered that the models are beneficial during the pre-planning stages, and may replace, or at the very least augment, the necessity for RDC field trials prior to fullscale construction. In addition, the analyses demonstrate that the AI techniques provide a feasible approach for non-linear modelling involving many parameters, which in turn, further encourages future applications in the broader geotechnical engineering context. Finally, a comprehensive set of guidelines for each of the AI techniques employed in this research, i.e. ANN and LGP, is provided, with the intention of assisting potential and current users of these techniques.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201