MAT-701: PREDICTING THE COMPRESSIVE STRENGTH OF ULTRA-LIGHTWEIGHT CONCRETE BY AN ARTIFICIAL NEURAL NETWORK

Ultra-lightweight concrete (ULWC) has potential applications for floating structures and architectural elements because of its dry density coming in at under 1000 kg/m3. The objective was to develop an artificial neural network (ANN) to aid the ULWC designer according to his needs. Boundary conditions were set for each material and 13 constraints based on the water binder ratio, density, air content, binder and aggregate content. The ANN predicted the compressive strength with a comfortable margin of error, with the gap encountered being attributed to variability in workability. Precise constraints and boundary conditions are needed to ensure a lower variability in workability. The ANN, coupled with a genetic algorithm, can generate millions of mixes for a given compressive strength in a short amount of time. The designer is able to choose mixes according to additional needs, such as the carbon footprint, absolute density, polymer content, cost, etc

Reliability Based Design Optimization of Concrete Mix Proportions Using Generalized Ridge Regression Model

This paper presents Reliability Based Design Optimization (RBDO) model to deal with uncertainties involved in concrete mix design process. The optimization problem is formulated in such a way that probabilistic concrete mix input parameters showing random characteristics are determined by minimizing the cost of concrete subjected to concrete compressive strength constraint for a given target reliability. Linear and quadratic models based on Ordinary Least Square Regression (OLSR), Traditional Ridge Regression (TRR) and Generalized Ridge Regression (GRR) techniques have been explored to select the best model to explicitly represent compressive strength of concrete. The RBDO model is solved by Sequential Optimization and Reliability Assessment (SORA) method using fully quadratic GRR model. Optimization results for a wide range of target compressive strength and reliability levels of 0.90, 0.95 and 0.99 have been reported. Also, safety factor based Deterministic Design Optimization (DDO) designs for each case are obtained. It has been observed that deterministic optimal designs are cost effective but proposed RBDO model gives improved design performance

Design optimization of precast-prestressed concrete road bridges with steel fiber-reinforcement by a hybrid evolutionary algorithm

[EN] In this paper, the influence of steel fiber-reinforcement when designing precast-prestressed concrete (PPC) road bridges with a double U-shape cross-section is studied through heuristic optimization. A hybrid evolutionary algorithm (EA) combining a genetic algorithm (GA) with variable-depth neighborhood search (VDNS) is formulated to minimize the economic cost and CO2 emissions, while imposing constraints on all the relevant limit states. The case study proposed is a 30-m span-length with a deck width of 12 m. The problem involved 41 discrete design variables. The algorithm requires the initial calibration. Moreover, the heuristic is run nine times so as to obtain statistical information about the minimum, average and deviation of the results. The evolution of the objective function during the optimization procedure is highlighted. Findings show that heuristic optimization is a forthcoming option for the design of real-life prestressed structures. This paper provides useful knowledge that could offer a better understanding of the steel fiber-reinforcement in U-beam road bridges.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (BRIDLIFE Project: BIA2014-56574-R) and the Research and Development Support Program of Polytechnic University of Valencia (PAID-02-15).Yepes, V.; Martí Albiñana, JV.; García-Segura, T. (2017). Design optimization of precast-prestressed concrete road bridges with steel fiber-reinforcement by a hybrid evolutionary algorithm. International Journal of Computational Methods and Experimental Measurements. 5(2):179-189. https://doi.org/10.2495/CMEM-V5-N2-179-189S1791895

Comparison of artificial intelligence methods for predicting compressive strength of concrete

Tlačna čvrstoća betona je značajan parametar u projektiranju betona. Točnim predviđanjem tlačne čvrstoće betona mogu se smanjiti troškovi i ostvariti uštede u vremenu. U ovom radu se na temelju šest raznih međunarodnih nizova podataka uspoređuje uspješnost predviđanja vrijednosti tlačne čvrstoće betona primjenom nekoliko metoda baziranih na umjetnoj inteligenciji (prilagodljivi neuroneizraziti sustav, algoritam slučajnih šuma, linearna regresija, klasifikacijsko i regresijsko stablo, regresija potpornih vektora, metoda najbližih susjeda i stroj za ekstremno učenje). Učinak tih metoda procjenjuje se pomoću koeficijenta korelacije, korijena srednje kvadratne pogreške, srednje apsolutne pogreške i srednje apsolutne postotne pogreške. Usporedni rezultati pokazuju da je prilagodljivi neuroneizraziti sustav uspješniji od ostalih u svim nizovima podataka.Compressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets

Development of sustainable concrete using iron ore tailings as sand replacement

The increasing demands for iron ore worldwide have resulted in the generation of billion tonnes of iron ore tailings (IOT) which were found in all the iron ore mining industries. Rapid increase in consumption of river sand due to the increased in construction activities over exploited the riverbeds. This has led to a range of problems which include: depletion of natural sand, increased riverbed depth, water table lowering, intrusion of salinity and destruction of river embankment. This study explored the possibility of using IOT as a replacement for natural river sand in concrete production. Laboratory investigations were conducted to evaluate the characterization of IOT materials in terms of microstructure, physical and chemical properties. Leaching behaviour of IOT materials was also determined. Furthermore, mix design and the evaluation of the fresh and hardened properties of the IOT concrete were executed. Series of concrete were prepared with IOT at a replacement level of 25%, 50%, 75% and 100%, using water to cement ratio (w/c) of 0.40 and 0.60. Fresh properties of mixtures in terms of concrete slumps and density were studied. The hardened properties examined are mechanical strengths, deformation characteristics, durability properties and corrosion measurement. Corrosion rate were evaluated using linear polarization techniques. Finally, microstructural tests in terms of X-ray Diffraction (XRD), Field emission scanning microscopy (FESEM), Fourier Transform Infrared Spectroscopy (FTIR) and Thermo gravimetric analysis (TGA) were concurrently conducted on control and IOT concrete in order to determine the interaction and effect of the IOT material that brings about the performance of the concrete. A correlation coefficient using fitted linear regression analysis was performed on compressive strength to evaluate the significant level of concretes containing IOT. Results showed that IOT affect mixture workability negatively. However, the inclusion of super plasticiser showed tremendous influence in increasing the workability and reduced this drawback. At 50% replacement, the compressive strength of the concrete at 28 days was 65.6 and 37.7 MPa for 0.40 and 0.60 w/c ratio, respectively, which shows an improvement of 9% and 12% over the concrete with river sand. Concrete with IOT indicates a good resistance to carbonation compared to control specimen. Linear polarization resistance (LPR) results indicates that, corrosion rates of 0.02 mm/year for IOT concretes were the same with control at 0.60 w/c ratio while 0.01 mm/year was observed for control at 0.40 w/c ratio. Considering all these test results, 50% river sand replacement with IOT resulted in concrete of excellent strength and adequate durability performance except for exposure to acid attack. However, it has the quality to be used as partial replacement of sand

Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches

The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle multiple objectives. The performances of the prediction and optimization methods are also compared in the paper. (C) 2008 Elsevier Ltd. All rights reserved

Numerıcal Modelıng And Experımental Evaluatıon Of Shrınkage Of Concretes Incorporatıng Fly Ash And Sılıca Fume

Rötre genellikle sertleşmiş betonun önemli bir özelliği olarak ele alınır. Kuruma sürecinde boşluk yapısında bulunan serbest ve emilmiş su kaybedilir. Betonun rötresi kısıtlandığı zaman betonda olşan gerilmelere bağlı olarak çatlak oluşumu gözlenir. Bu çatlaklardan zararlı maddelerin geçmesiyle betonun dayanım ve dayanıklılıgında azalma olur. Bu çalışman ilk aşamasinda genetik programlama ve yapay sinir ağları yöntemleri kullanılarak rötre tahmin modelleri geliştirilmiştir. Modellerin eğitimi ve test edilmesi için literatürden veri toplanmıştır. Çalışmanın ikinci aşamasında ise uçucu kül ve silis dumanı içeren betonlar hazırlanarak kırk günlük kuruma sürecinde rötreleri ölçülmüştür. En yüksek rötre değerleri en çok mineral katkı içeren betonlarda gözlenmiştir. Bunların yanı sıra deneysel çalışmada elde edilen sonuçlar tahmin modellerinin verdikleriyle karşılaştırılmışlardır. YSA ile elde edilen değerlerin GP ile elde edilenlere göre gerçeğe daha yakın oldukları görülmüştür

Modeling Asphalt Pavement Overlay Transverse Cracks and High Performance Concrete Using Levenberg-Marquardt Genetic Operation Tree

為實驗數據建立模型一般是使用類神經演算法與非線性迴歸，然而類神經演算法被批評是一種黑盒子，無法提供研究者了解變數之間明確的關係及物理意義。而非線性迴歸則是需要研究者預先決定正確的方程式結構方能調整係數以達 RMSE( Root of Mean Square Error )誤差最小值，可惜的是，一組明確可定義的方程式不容易預先猜測。為解決這些難處，近期研究採用基因運算樹( Genetic Operation Tree; GOT )自動擬定方程式結構，然其過低的係數搜尋效率致使在染色體迭代過程中，往往把『差係數值配好方程式結構』的可行解淘汰，並導向以更多運算子、運算元去磨合實驗數據，其成果是公式冗長且 RMSE 較大。本研究提出一創新技術，結合二種最佳化方法： Levenberg-Marquardt 演算法與 GOT ，我們稱之 LMGOT 。 GOT 用基因演算法求解離散問題，以找尋較佳的運算樹結構。一株運算樹結構可代表一組方程式，同時再利用 LM 方法求解出方程式係數的最佳值，有了係數值後，實驗數據代入方程式所得到的預估值可與實驗真值相較，其 RMSE 誤差值就是 GOT 的適存值，結合 GOT 的迭代技巧，可逐代逼近最佳的運算樹結構。本研究將 LMGOT 方法應用在美國德州交通部收集十五年的路面服務性能實驗數據及兩種不同實驗室來源的高性能混凝土數據，實驗數據可在 http://em.nchu-cm.com/ 下載。成果顯示 LMGOT 可產生具預測性的簡潔公式，且能有效率地建立低 RMSE 的瀝青鋪面橫向裂縫總長度及高性能混凝土抗壓強度的預測模型。The Artificial Neural Network ( ANN ) and the nonlinear regression method ( NRM ) are commonly used to build models from experimental data. However, the ANN has been criticized for incapable of providing clear relationships and physical meanings, and is usually regarded as a black box. The NRM needs predefined and correct formula structures to process parameter search in terms of the minimal sum of square errors. Unfortunately, the formula structures of these models are often unclear and cannot be defined in advance. To overcome these challenges, recent studies have applied genetic operation tree ( GOT ) to automatically set a formula structures. The shortcoming of GOT is so low efficiency on search coefficient. It usually eliminates a feasible solution with composition of wrong coefficient and good structure in the iteration process of chromosomes and directs to use more operators and operands to build model that has tedious expression and higher RMSE. This study proposes a novel approach, called “ LMGOT ”, that integrates two optimization techniques: the Levenberg–Marquardt ( LM ) Method and the genetic operation tree. The GOT borrows the concept from the genetic algorithm, a famous algorithm for solving discrete optimization problems, to generate operation trees ( OTs ), which represent the structures of the formulas. Meanwhile, the LM takes advantage of its merit for solving nonlinear continuous optimization problems, and determines the coefficients in the GOTs that best fit the experimental data. This paper uses the LMGOT to investigate the data sets of pavement cracks from a 15-year experiment conducted by the Texas Departments of Transportation. Results show a concise formula for predicting the length of asphalt pavement transverse cracks, and indicate that the LMGOT is an efficient approach to building an accurate crack model. In the other two HPC compress strength cases from difference sources, it also shows the same result.1 前言 13 2 文獻回顧 19 2.1 實驗數據模型之應用方法 19 2.2 運算樹( Operation Tree ) 22 2.3 基因運算樹( Genetic Operation Tree ) 24 2.3.1 浮動層級式運算樹結構 25 2.4 求解非線性函數係數值 27 2.4.1 陡降法(The Steepest Descent method) 29 2.4.2 牛頓法( Newton method ) 30 2.4.3 高斯-牛頓法( Gauss-Newton method ) 30 2.4.4 Levenberg-Marquardt method 31 3 LMGOT 方法 33 3.1 LMGOT 流程說明 34 3.1.1 設定執行參數值 34 3.1.2 初始化運算樹 35 3.1.3 最佳化方程式係數值 37 3.1.4 評估運算樹的適存值 40 3.1.5 評估 GOT 是否收斂 40 3.1.6 依適存值選擇菁英 41 3.1.7 運算樹繁殖 41 3.1.8 產生子代並進行係數最佳化 47 3.1.9 運算樹最佳解輸出 48 3.2 LMGOT 方法小結 48 4 案例實作 49 4.1 案例 1: SPS-5 的 A502-509 鋪面資料 49 4.1.1 表面處理 53 4.1.2 加鋪厚度 53 4.1.3 再生材料 53 4.1.4 基因編碼說明 53 4.1.5 SPS-5 之橫向裂縫總累積長度模型 55 4.1.6 探討公式內變數關係 65 4.1.7 SPS-5 案例小結 67 4.2 案例 2: Lim Chul-Hyun(2004) 的高性能混凝土資料 69 4.2.1 LMGOT 之抗壓強度模型 71 4.2.2 Adil(2009) 之抗壓強度模型 76 4.2.3 Lim(2004) 數據之抗壓強度模型小結 77 4.2.4 Lim(2004) 實驗數據的坍度模型 80 4.3 案例 3: Yeh I-Cheng(2006) 的高性能混凝土數據 82 4.3.1 Yeh(2006) 實驗數據之抗壓強度模型 82 4.3.2 Yeh(2006) 案例小結 86 5 結論 89 6 未來方向與建議 93 7 參考文獻 95 8 附錄 10

Otimização estocástica multi-objetivos na produção de Cimento Portland com co-processamento de resíduos e adição de mineralizadores.

A produção de cimento Portland é um processo complexo que envolve matérias–primas específicas e elevado consumo de energia, tanto térmica quanto elétrica, representando elevado custo de produção. Como combustíveis alternativos para a indústria cimenteira têm se utilizado resíduos industriais. A principal vantagem de se utilizar técnicas de coprocessamento está na possibilidade de reduzir o consumo de combustíveis tradicionais e matérias-primas primárias e proporcionar a redução de disposição de resíduos no meio ambiente. Estudos revelam que determinados resíduos podem conter substâncias mineralizadoras. Os mineralizadores diminuem o calor de reação necessário à formação do clínquer, permitindo uma redução da temperatura de chama dentro do forno rotativo, o que implica numa redução do consumo de combustível e até a substituição por combustíveis de menor poder calorífico. Para se obter uma mistura destas variáveis considerando as restrições de ordem operacional e ambiental, técnicas de otimização são necessárias. Para que os resultados obtidos fossem robustos, foi realizado um estudo da variabilidade das variáveis para se determinar as variáveis como estocásticas ou determinísticas. As variáveis conhecidas como estocásticas foram analisadas por superfície de resposta e foram obtidas as funções de média e variância da função custo de produção. Para otimizar os dados obtidos foi utilizado o algoritmo Controlled Random Search Algorithm – CRSA. Foi aplicado otimização multiobjectivo para se otimizar a função custo de produção juntamente com as funções média e variância. O estudo mostra ser possível empregar três tipos de resíduos simultaneamente, obtendo um custo robusto dentro do intervalo analisado. A mistura destes resíduos permite uma redução no consumo de matéria prima em 6000 ton por mês. A introdução de resíduos como combustível permite a redução de combustível tradicional. Os resultados mostram que é possível obter um cimento de qualidade através das misturas que se obteve da otimização. A introdução de resíduos como substitutos parciais de matérias-primas representa uma economia de aproximadamente 5% na introdução de matérias-primas tradicionais. O resíduo fosfogesso pode ser adicionado ao processo mantendo a qualidade do produto final. A introdução de resíduos como combustível secundário permite a redução de combustível primário como o carvão mineral