Preliminary Evaluation of Cement Mortars Containing Waste Silt Optimized with the Design of Experiments Method

materials

Article Preliminary Evaluation of Cement Mortars Containing Waste Silt Optimized with the Design of Experiments Method

Abbas Solouki 1,2,* , Giovanni Viscomi 2, Piergiorgio Tataranni 1,* and Cesare Sangiorgi 1

1 Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, 40136 Bologna, Italy; [email protected] 2 S.A.P.A.B.A. srl (Società Anonima Prodotti Asfaltico Bituminosi Afﬁni), 40037 Pontecchio Marconi, Italy; [email protected] * Correspondence: [email protected] (A.S.); [email protected] (P.T.)

Abstract: Every year, up to 3 billion tons of non-renewable natural aggregates are demanded by the construction sector and approximately 623 million tons of waste (mining and quarrying) was produced in 2018. Global efforts have been made to reduce the number of virgin aggregates used for construction and infrastructure sectors. According to the revised waste framework directive in Europe, recycling at least 70% of construction and demolition waste materials by 2020 was obligatory for all member states. Nonetheless, quarries must work at full capacity to keep up with the demands, which has made quarry/mining waste management an important aspect during the past decades. Amongst the various recycling methods, quarry waste can be included in cement mortar mixtures. Thus, the current research focuses on producing cement mortars by partially substituting natural sand with the waste silt obtained from the limestone aggregate production in S.A.P.A.B.A. s.r.l. (Italy). A Design of Experiments (DOE) method is proposed to define the optimum mix design, aiming to include waste silt in cement mortar mixtures without affecting the final performance. Three cement Citation: Solouki, A.; Viscomi, G.; mortar beams were produced and tested for each of the 49 randomized mixtures defined by the DOE Tataranni, P.; Sangiorgi, C. method. The obtained results validate the design approach and suggest the possibility of substituting Preliminary Evaluation of Cement up to 20% of natural sand with waste silt in cement mortar mixtures. Mortars Containing Waste Silt Optimized with the Design of Keywords: cement mortar; DOE; waste silt; quarry waste; mixture design; aggregate recycling Experiments Method. Materials 2021, 14, 528. https://doi.org/10.3390/ ma14030528 1. Introduction Academic Editor: Marijana Hadzima-Nyarko The first step in every quarrying process starts with the stripping stage, where the Received: 28 December 2020 unwanted materials are removed from the Earth’s surface. Based on the quality and type of Accepted: 19 January 2021 the rocks, up to three different crushing stages could be applied during aggregate produc- Published: 22 January 2021 tion. At the final step, the crushed aggregates go through the washing and screening stage, which is vital for quality and gradation control of the materials. The water used during Publisher’s Note: MDPI stays neu- the washing process is directed to precipitation tanks for further processing. The resulting tral with regard to jurisdictional clai- residues which mainly consist of dirt and very fine powdery substances are piped out into ms in published maps and institutio- artificial lakes and ponds called tailing mines or sedimentation lakes. These materials, nal affiliations. mostly mineral fillers from plant processes, could become an environmental issue due to the landfilling limitations and strict legislation on their disposal [1]. Every year, up to 3 billion tons of non-renewable natural aggregates are demanded by the construction sector [2] and approximately 623 million tons of waste (mining and quarrying) were produced in Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. 2018 [3]. Global efforts have been made to reduce the number of virgin aggregates used for This article is an open access article construction and infrastructure sectors. For instance, based on the revised waste framework distributed under the terms and con- directive in Europe recycling of at least 70% of construction and demolition waste materials ditions of the Creative Commons At- by 2020 was obligatory for all member states [2]. Nonetheless, quarries must work at tribution (CC BY) license (https:// full capacity to keep up with the demand for raw materials. Therefore, quarry/mining creativecommons.org/licenses/by/ waste management has become an important aspect during the past decades and various 4.0/). methods have been proposed for reducing its impacts on the environment.

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Quarry waste has been used in various applications such as geopolymer production [4], soil stabilization [5], pavements [6,7] and production of artificial aggregates [8]. Studies and experimental applications have also highlighted the possibility of using quarry waste in cementitious materials. For instance, Cavaleri et al. (2018) partially substituted sand with quarry dust to produce concrete. The results indicated that the substitution of 13% of sand with limestone quarry dust could improve the mechanistic properties of the cement, whereas 26% of quarry dust led to lower mechanical properties [9]. The inclusion of quarry dust in cement bound materials and its effect on various properties of cement such as transport, dimensional stability, alkali–silica reaction, the heat of hydration and color were investigated. Medina et al. [10] found no adverse effects from the addition of quarry dust into the mixture and suggested its use in the design of new type II/A conventional as well as type II/A special low heat cement. These alternative applications could potentially reduce the environmental impact of using raw materials and of course decrease the impact of landfilling industrial waste fillers. Felekoglu (2007) incorporated quarry dust limestone powder in Self-Compacting Concrete (SCC) and paste applications with the aim of reducing the environmental impact of the waste quarry powder [11]. Various physical and mechanical properties of the cement paste were examined, and the performance of the mixture indicated the possibility of using up to 10% of limestone quarry dust in normal-strength self-compacting cement. Similar work on self-compacting concrete was carried on by Uysal et al. [12], where Portland cement was partially replaced with different quarry dust including limestone, basalt, and marble. The results were similar to previous studies which suggested the economic feasibility of using the mentioned powders in SCC production [11,12]. In a different attempt to manage mine waste, a study was conducted aiming to investigate the possibility of using gold waste rocks as construction materials [13]. The waste materials were collected from a gold open-mine site located in the Abitibi-Temiscamingue region (QC, Quebec, Canada) and the material was used for concrete production. The obtained compressive strength for concretes made with waste rocks and natural sand and gravel were comparable after 28 and 56 days of curing [13]. In a separate study, limestone dust was substituted by 30% of the total concrete weight to produce lightweight concrete. The mechanical properties of the synthetic material were tested and the result showed acceptable performance [14]. However, the authors indicated that further work and tests regarding the mechanical properties of the lightweight concrete are required. Concrete and mortars are produced using different design methods. For instance, various common approaches have been reviewed for the production of self-compacting cement [15]. the mixture could be designed based on empirical methods. Compressive strength or rheological properties of the paste could also determine the mixture design. However, an approach that has not been fully exploited is the use of statistical models for concrete mixture design. For instance, the effect of different ingredients such as glass fiber, metakaolin (MK), paste and silica sand on the flexural and compressive strength of glass fiber (GF)-reinforced concrete (GRC) was studied [16]. The authors also applied the Taguchi method to optimize the final mix design based on four factors including aggregate (SS)-to-cement ratio (A/C), GF, W/C and MK contents. The data indicated that SS, MK, GF and CP contents significantly affected the final strength of the concrete mixtures. However, most statistical approaches are limited to factorial designs [17,18] and response surface methods [19,20] and less attention has been paid to mixture-design of experiments methods. It is by the use of a mixture-design approach that one could simultaneously study the effects of all the components and their interactions. The effect of including fly ash (FA), nano-silica (nS), and recycled plastic on the mechanical properties and cost of concrete was investigated by applying two different mixture design approaches [21]. The first model was produced from a screening model and the results were then used as an input for the sequential optimization. Based on the models, the authors suggested that by adding 2.5% of nS and 10% of FA about 44% of coarse aggregates could be substituted by plastic. A simple lattice mixture design having three factors and five levels was applied to study the Materials 2021, 14, x FOR PEER REVIEW 3 of 17

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adding 2.5% of nS and 10% of FA about 44% of coarse aggregates could be substituted by plastic. A simple lattice mixture design having three factors and five levels was applied to effectstudy of threethe effect different of three sand different types sand on self-compacting types on self-compacting cement properties cement properties [22]. The [22]. models The indicatedmodels anindicated increase an in increase compressive in compressive strength stre withngth an increasewith an increase in crushed in crushed sand proportions. sand pro- However,portions. when However, dune when sand dune was used,sand was the resultingused, the resulting strength valuesstrength dropped. values dropped. TransformingTransforming quarry quarry dust/waste dust/waste into into secondary secondary materials materials could could give agive second a second chance to wastechance materials, to waste materials, contribute contribute to waste to management, waste management, balance balance natural natural resource resource utilization uti- andlization produce anda produce circular a economycircular economy for the for construction the construction sector sector [23–26 [23–26].]. Thus, Thus, the the current cur- researchrent research focused focused on producing on producing cement cement mortars mortars by by substituting substituting natural natural sandsand with the the wastewaste silt silt obtained obtained from from limestone limestone aggregate aggregate production production S.A.P.A.B.A.S.A.P.A.B.A. s.r.l. (Pontecchio Marconi,Marconi Italy). , Italy).

2. Materials2. Materials LimestoneLimestone aggregates aggregates are are generally generally excavated excavated fromfrom looseloose earth and transported to to thethe production production plant plant in in S.A.P.A.B.A S.A.P.A.B.A s.r.l. s.r.l. (Societ (Societàà Anonima Anonima ProdottiProdotti Asfaltico Bituminosi Bituminosi Afﬁni)Affini) During During the the washing washing process, process, unwanted unwanted substances substances such such as siltas silt and and clay clay are separatedare sepa- fromrated the from limestone the limestone aggregates. aggregates. The silty The materials silty materials are then are then piped piped and and landﬁlled landfilled in thein sedimentationthe sedimentation lakes. lakes. For the For current the current study, study, the silt the was silt excavated was excavated from from S.A.P.A.B.A.’s S.A.P.A.B.A.’s lakes, dried,lakes, crushed, dried, crushed, sieved and sieved stored and in stored sealed in plasticsealed plastic bags for bags further for further use (Figure use (Figure1). 1).

FigureFigure 1. Silt 1. Silt obtained obtained from from sedimentation sedimentation lakes lakes at at S.A.P.A.B.A. S.A.P.A.B.A. spa, spa, Bologna Bologna (Italy).(Italy).

TheThe elemental elemental and and compound compound analysis analysis ofof thethe wastewaste siltsilt waswas examined using using X-ray X-ray ﬂuorescencefluorescence (XRF) (XRF) (Shimadzu, (Shimadzu, Kyoto, Kyoto, Japan) Japan) and X-rayand powderX-ray powder diffraction diffraction (XRD) (Rigaku,(XRD) Tokyo,(Rigaku, Japan), Tokyo, respectively. Japan), respectively. The raw materialThe raw material had a pH had of a 7.0, pH where of 7.0, where almost almost 60% of 60% the chemicalof the chemical composition composition is made is upmade of SiOup of2 and SiO2Al and2O Al3 (Table2O3 (Table1). Moreover, 1). Moreover, the the number number of hazardousof hazardous materials materials such such as arsenic, as arsenic, barium, barium, beryllium, beryllium, cadmium, cadmium, cobalt, cobalt, chrome, chrome, mercury, mer- nickel,cury, lead nickel, and lead copper and copper were within were within the Italian the Italian legal limits. legal limits. The mineralogical The mineralogical evaluation eval- (Figureuation2) (Figure revealed 2) therevealed presence the ofpresence quartz (32%),of quartz calcite (32%), (28%), calcite illite/micas (28%), illite/micas (21%), chlorite (21%), (5%)chlorite with traces(5%) with of dolomite traces of anddolomite feldspars. and feldspars.

TableTable 1. Chemical 1. Chemical oxides oxides present present in in waste waste silt. silt.

ParameterParameter ValueValue (%) (%) SiO2 43.5 SiO2 43.5 TiO2 0.6 TiO2 0.6 Al2AlO32O3 12.512.5 Fe2FeO32O3 6.16.1 MnOMnO 0.20.2 CaOCaO 15.815.8 Na2O 1.0 Na2O 1.0 K2O 1.9 2 P2OK5 O 0.11.9 MgOP2O5 3.00.1 MgO 3.0

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6000

6000 5000

5000 4000

4000 3000

3000

Intensity (Counts) Intensity 2000

Intensity (Counts) Intensity 2000 1000 1000 0 0 1020304050600 0 1020304050602 Theta/Degree 2 Theta/Degree Figure 2. WasteFigure silt XRD 2. analysisWaste silt revealing XRD analysis a crystalline revealing structure a crystalline observed structure as sharp observed peaks asin the sharp diagram. peaks in the diagram. Figure 2. Waste silt XRD analysis revealing a crystalline structure observed as sharp peaks in the diagram. Natural limestoneNatural sand limestone (0–4 mm) sand was (0–4 used mm) for the was cement used for mortar the cement production. mortar The production. The gradation curvegradation is shownNatural curve in limestone Figure is shown 3. sand The in (0–4obtained Figure mm)3 values.was The used obtainedfor forbulk the density valuescement (g/cm formortar bulk3) (UNIproduction. density (g/cm The 3) 1097-6), sand (UNIequivalentgradation 1097-6), curve (uni sand is933-8) shown equivalent and in harmfulFigure (uni 3. 933-8) finesThe obtained and(gMB/KG) harmful values (UNI ﬁnes for 933-9) (gMB/KG)bulk densitywere (UNIre- (g/cm 933-9)3) (UNI were ported as 2.658,reported1097-6), 90 and sand0.5, as 2.658,resp equivalentectively. 90 and (uni 0.5,The respectively.933-8)light organic and harmful Theimpu lightrities fines organic were (gMB/KG) reported impurities (UNI as 0.07933-9) were reportedwere re- as which was well0.07ported below which as 2.658,the was 0.5 90 well andlimits. below 0.5, Finally,resp theectively. 0.5 a limits. 42.5 The R Finally,light white organic aPortland 42.5 impu R white cementrities Portland were (Buzzi reported cement as 0.07 (Buzzi Unicem, CasaleUnicem,which Monferrato, was Casale well Italy) Monferrato, below and the a specia 0.5 Italy) limits.lly and designed aFinally, specially additive a 42.5 designed Rthat white allows additive Portland the that use cement allows of the(Buzzi use of swelling claysswelling Unicem,or aggregates Casale clays orwith Monferrato, aggregates very high Italy) with fines and very were a highspecia used. ﬁneslly designed were used. additive that allows the use of swelling clays or aggregates with very high fines were used.

Figure 3. LimestoneFigure sand 3. Limestone gradationFigure 3. sandcurve.Limestone gradation sand curve. gradation curve.

3. Methods 3.3. Methods 3.1. Design of Experiments 3.1. Design of Experiments3.1. Design of Experiments In every research or discovery process, the development of experiments is fundamental In every researchIn every or discovery research process,or discovery the developmentprocess, the development of experiments of experiments is funda- is funda- to achieving results. Each experiment has a determined number of variables with a certain mental to achievingmental results. to achieving Each experimentresults. Each has experiment a determined has a numberdetermined of variables number of with variables with number of levels, where the effect of each variable is investigated on desirable outcomes. a certain numbera certain of levels, number where of levels,the effect where of eachthe effect variable of each is investigated variable is investigated on desirable on desirable Scientists have been using experimental design methods to ﬁt data to empirical functions outcomes. Scientistsoutcomes. have Scientists been using have experiment been usingal experiment design methodsal design to fit methods data to to empirical fit data to empirical and provide information about a system or a process. Experimental designs are an approach functions and providefunctions information and provide about information a system about or a a process. system orExperimental a process. Experimental designs are designs are that facilitates the selection of variables and levels in such a way that the variations and an approach thatan approachfacilitates that the facilitatesselection theof variables selection andof variables levels in and such levels a way in suchthat athe way that the errorsvariations are minimized.and errors are minimized. variations and errors are minimized. As for the ﬁrst step in any Design of Experiments (DOE), the purpose of the experiment should be determined. In some cases, optimization of a process or response(s) is the main

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goal, whereas in some studies the effect of certain factors and their levels on the outcome is the focus. Therefore, each experiment and DOE will have a specific purpose and need to be tackled differently. The boundaries of an experiment are defined and restrained based on selected ranges of the variables. For instance, selecting a testing temperature between 20 to 80 ◦C for a specific experiment will define its experimental region. The design will be finalized by determining the number of levels for the corresponding factors. A Response Surface Methodology (RSM) [27] is a statistical tool, which investigates the relationship between variables of an experiment with the produced outcomes. Com- pared to a full factorial design, RSM can significantly reduce the number of required experimental runs. In many studies, the aim is to select the ingredient’s proportion of a blend. In such cases, the implementation of a mixture design (a special type of RSM) could effectively determine the dependent variables’ proportions that will produce the desired response. In a mixture design experiment, the dependent factors are proportions of different components of a blend and the total sum of all factors for each run will be equal to 1 or 100% (Equation (1)). The mixture DOE allows studying the effects of different ingredients/factors on different responses such as compressive and flexural strength of the mortar specimens. However, in such cases, applying standard design will require a higher number of experiments to find the final mix design. An n-component mixture is presented in Equation (1). n 0 ≤ xi ≤ 1 i = 1, 2, 3, . . . , n ∑ xi = 1 (1) i=1

where the proportion of the ith ingredient/factor is represented by xi. Two of the most widely used mixture designs are the simplex lattice and simplex centroid designs. The former design is applied when the components/ingredients are equally spaced within the design. Furthermore, in such cases, a full cubic analysis could be applied. However, only special cubic estimates could be conducted for centroid design. In both cases, no constraints or boundary limits are set. On the other hand, an extreme vertex design could only be used when linear or boundary constraints are set for the components. In such cases, the mixture design will only cover a small proportion within the simplex design and usually occurs when components have upper and lower boundaries. Each DOE has its specific requirements and inputs. For the current study, a mixture DOE having 5 different components including cement, water, sand, silt and additive was proposed. Due to the high number of factors, the extreme vertices design was used rather than the simplex centroid design. Extreme vertices design only covers small spaces in the simplex design and could be adapted when the design space is not linear. However, not much literature has been found regarding extreme vertices design for cement bound materials [16,21]. To produce the mixture DOE, the five components and their corresponding boundaries were inputted into JMP® software. The determined upper and lower boundaries (total weight of mixture) for cement, water, sand, silt and additive were as 22–28%, 12–20%, 43–61%, 2–20% and 0.17–0.35%, respectively. As mentioned above extreme vertices design was selected. Consequently, a DOE having 49 randomized runs was produced, where for each run 3 specimens from the same batch were produced. The average value for the corresponding strength (compressive or flexural) was used as inputs to produce the models. Therefore, after inputting the flexural strength (FS) and Unconfined Compressive Strength (UCS) values in the DOE, two corresponding models were produced. The data analysis was also carried out in JMP® software (Version 14.0. SAS Institute Inc., Cary, NC, USA, 1989–2019). Up to five levels of interactions were chosen and the forward selection with Akaike Information Criteria (AIC) was applied to determine the significance of the models. The model was produced, and the optimum results were indicated using the prediction profiler tool for both compressive and flexural strength measurements. The desirability factor was based on the maximum usage of silt in the mixture. Therefore, the value of the silt was set to 20% and the corresponding values for the remaining components were calculated according to the obtained models. Materials 2021, 14, x FOR PEER REVIEW 6 of 17

models. The model was produced, and the optimum results were indicated using the prediction profiler tool for both compressive and flexural strength measurements. The desirability factor was based on the maximum usage of silt in the mixture. Therefore, the value Materials 2021, 14, 528 of the silt was set to 20% and the corresponding values for the remaining components6 of 16 were calculated according to the obtained models.

3.2. Sample Preparation The samplessamples werewere prepared prepared by by mixing mixing Cement, Cement, sand sand and and silt silt before before adding adding water water and andthe additivethe additive (Figure (Figure4). The 4). mortar The mortar was poured was poured into metal into metal molds molds (4 × 4 (4× ×16 4 × cm) 16 andcm) wasand wascured cured at room at room temperature temperature for 28 for days. 28 Thedays. samples The samples were ﬁrst were tested first for tested ﬂexural for strengthflexural strengthresulting resulting in each beam in each to beam be broken to be into broken half. into Each half. half Each was half then was tested then for tested compressive for com- pressivestrength. strength. The whole The procedure whole procedure was based was on based EN 1015-11:2019 on EN 1015-11:2019 (E) standard. (E) standard.

Figure 4. Figure 4. Cement mortarmortar samplesample production production and and strength strength (flexural (flexural and and compressive) compressive) testing testing procedure. procedure.4. Results and Discussion

4. ResultsAn efficient and Discussion way of studying a mixture in which all components sum to 1 (100%) is to use the mixture design approach. With this method, the changes in the ingredients of a mixtureAn efficient or blend way and of the studying resulting a effectsmixture on in the which responses all components could be explored.sum to 1 (100%) Thus, by is toapplying use thethe mixture mixture design design appr concept,oach. With the effect this method, of cement, the water, changes sand, in siltthe andingredients the additive of a mixtureon the responses or blend includingand the resulting compressive effects and on flexural the responses strength could were be investigated. explored. Thus, by applyingThe initialthe mixture model design was constructed concept, the based effect on 49of randomizedcement, water, runs. sand, The silt order and of the the addi- tests wastive on randomized the responses to reduce including the chance compressive of bias inand the flexural results strength that could were have investigated. occurred due to differencesThe initial in materials model was or experimental constructed conditions.based on 49 However, randomized to improve runs. The the order accuracy of the of teststhe models was randomized and based to on reduce the residual the chance plots, of a bias total in of the 10 results runs were that identifiedcould have as occurred outliers dueand wereto differences eliminated in frommaterials the model or experimental [28]. Consequently, conditions. the secondHowever, attempt to improve improved the theac- curacyoverall of accuracy the models of the and models based up on to the 20%. residual The summary plots, a total of fit of and 10 the runs residual were identified plots for the as outliersmodels areand presented were eliminated in Tables from2 and the3 andmodel Figures [28]. Consequently,5 and6. The results the second indicated attempt an R im-2 valueproved of the 95.5 overall and 91.2% accuracy for the of the models models related up to to 20%. UCS The and summary FS, respectively. of fit and the residual plots for the models are presented in Tables 2 and 3 and Figures 5 and 6. The results indi- Tablecated 2.anSummary R2 valueof of fit. 95.5 Effect and of 91.2% components for the on models Unconfined related Compressive to UCS and Strength FS, respectively. (UCS).

Table 2. Summary of fit.Indicator Effect of components on Unconfined Compressive Value Strength (UCS). R Square 0.955208 Indicator Value R Square Adj 0.925996 Root Mean SquareR Square Error 3.106147 0.955208 Mean of ResponseR Square Adj 25.78675 0.925996 Root Mean Square Error 3.106147 Mean of Response 25.78675

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Table 3. Summary of fit. Effect of components on flexural strength. Table 3. Summary of fit. Effect of components on flexural strength. Table 3. Summary of fit. Effect of components on flexural strength. Table 3. Summary of ﬁt. EffectIndicator of components on ﬂexural strength. Value Indicator Value IndicatorR Square Value 0.911509 IndicatorR Square Value 0.911509 RR Square Square Adj 0.911509 0.879905 R Square Adj 0.879905 RootR SquareR Mean Square Square Adj Error 0.911509 0.879905 0.795718 RootR Square Mean Adj Square Error 0.879905 0.795718 RootMean Mean of Square Response Error 0.795718 5.679888 Root MeanMean Square of Response Error 0.795718 5.679888 ObservationsMean of Response (or Sum Wgts) 5.679888 39 MeanObservations of Response (or Sum Wgts) 5.679888 39 ObservationsObservations (or Sum (or Wgts) Sum Wgts) 39 39

Figure 5. Residual plot for UCS model. FigureFigure 5. 5. ResidualResidual plot plot for for UCS UCS model. model. Figure 5. Residual plot for UCS model.

FigureFigure 6.6. ResidualResidual plotplot forfor thethe flexuralflexural strengthstrength model.model. Figure 6. Residual plot for the flexural strength model. Figure 6. Residual plot for the flexural strength model. TheThe designdesign ofof experimentsexperiments consistingconsisting ofof 3939 randomizedrandomized runsruns is presentedpresented in Table4 4.. The design of experiments consisting of 39 randomized runs is presented in Table 4. TheThe flexuralflexural and and UCS UCS were were inputted inputted into into the th DOEe DOE resulting resulting in a modelin a model (equation). (equation). For each For The Theflexural design and of UCS experiments were inputted consisting into ofth 39e DOE randomized resulting runs in ais model presented (equation). in Table For 4. ofeach the of 39 the runs, 39 theruns, obtained the obtained model model was used was toused calculate to calculate the FS the and FS UCS, and UCS, which which are also are Theeach flexural of the 39 and runs, UCS the were obtained inputted model into was th eused DOE to resulting calculate inthe a FSmodel and UCS,(equation). which For are includedalso included in the in table the table as predicted as predicted FS and FS predicted and predicted UCS. TheUCS. actual The actual versus versus the predicted the pre- eachalso includedof the 39 runs,in the the table obtained as predicted model FSwas and used predicted to calculate UCS. the The FS actual and UCS, versus which the pre-are alsovaluesdicted included forvalues flexural in for the flexural andtable UCS as and predicted are UCS also are depictedFS also and depicted predicted in Figure in FigureUCS.7, where The 7, where theactual blue the versus line blue indicates theline pre- indi- dictedthecates average thevalues average value for flexural forvalue both andfor UCS both UCS and UCSare FS. also and The depicted graphsFS. The indicate ingraphs Figure aindicate linear7, where relationship a thelinear blue relationship line between indi- dictedcates the values average for flexural value forand both UCS UCS are alsoand depictedFS. The graphsin Figure indicate 7, where a linearthe blue relationship line indi- catesactualbetween the and average actual predicted and value predicted values for both which values UCS is alsowhichand backedFS. is alTheso up backedgraphs by the upindicate high by reliabilitythe a high linear reliability ofrelationship the models of the between(Rmodels2). The (Ractual calculated2). The and calculated predicted models formodels values UCS for andwhich UCS FS is are and also presented FS backed are presented up as Supplementaryby the as high supplementary reliability Data. of data. the betweenmodels (R actual2). The and calculated predicted models values for which UCS is and also FS backed are presented up by the as high supplementary reliability of data. the models (R2). The calculated models for UCS and FS are presented as supplementary data.

FigureFigure 7.7. ActualActual versusversus predictedpredicted valuesvalues for:for: ((aa)) compressive;compressive; ((bb)) ﬂexuralflexural strength. strength. Figure 7. Actual versus predicted values for: (a) compressive; (b) flexural strength. Figure 7. Actual versus predicted values for: (a) compressive; (b) flexural strength. Table 4. Design of experiments data. Table 4. Design of experiments data. Table 4. Design of experiments data.

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Table 4. Design of experiments data.

Cement (%) Water (%) Sand (%) Silt (%) Additive (%) FS (MPa) UCS (MPa) Pred. FS (MPa) Pred. UCS (MPa) 1 22.00 20.00 55.65 2.00 0.35 0.00 0.00 −0.31 0.60 2 22.00 14.65 43.00 20.00 0.35 3.83 18.17 4.16 16.95 3 24.64 13.56 55.79 5.79 0.215 6.08 30.01 6.92 31.50 4 28.00 20.00 43.00 8.83 0.17 4.91 19.86 5.70 22.73 5 24.83 12.00 61.00 2.00 0.17 9.04 34.59 8.06 36.96 6 22.00 14.83 43.00 20.00 0.17 4.51 18.94 3.94 19.23 7 22.00 12.00 45.65 20.00 0.35 4.88 20.95 4.37 17.83 8 22.00 12.00 61.00 4.65 0.35 8.20 30.82 8.68 34.47 9 28.00 20.00 43.00 8.65 0.35 5.39 18.45 4.38 14.57 10 28.00 20.00 49.83 2.00 0.17 3.91 16.05 3.03 11.78 11 23.23 13.56 48.21 14.79 0.215 6.45 27.41 6.43 28.84 12 22.00 14.83 61.00 2.00 0.17 7.19 32.48 6.60 30.93 13 24.55 13.56 46.79 14.79 0.305 5.20 30.78 6.36 26.95 14 23.23 17.56 53.12 5.79 0.305 4.77 25.15 4.19 23.09 15 26.23 17.56 46.79 9.21 0.215 6.68 30.55 6.47 31.59 16 22.00 12.00 45.83 20.00 0.17 3.40 17.48 3.95 21.04 17 23.23 13.56 48.12 14.79 0.305 7.07 30.66 6.65 28.65 18 26.23 13.56 46.79 13.12 0.305 6.84 32.21 7.21 33.33 19 28.00 12.00 43.00 16.83 0.17 2.85 8.89 3.26 8.95 20 22.00 20.00 43.00 14.65 0.35 4.49 21.32 4.59 21.30 21 22.00 12.00 61.00 4.83 0.17 7.70 40.45 8.04 36.64 22 28.00 12.00 43.00 16.65 0.35 4.80 20.59 3.95 21.84 23 23.23 14.98 55.79 5.79 0.215 6.72 32.31 5.93 28.51 24 22.00 20.00 43.00 14.83 0.17 5.35 21.53 5.93 21.70 25 23.23 17.56 53.21 5.79 0.215 5.20 25.96 4.34 23.66 26 26.23 17.56 50.12 5.79 0.305 6.02 29.26 5.47 27.90 27 23.23 14.98 46.79 14.79 0.215 6.02 26.86 6.33 29.60 28 22.00 14.65 61.00 2.00 0.35 7.46 29.35 7.15 30.08 29 24.64 13.56 46.79 14.79 0.215 6.25 28.46 6.13 25.89 30 23.23 14.89 46.79 14.79 0.305 6.95 27.17 6.47 29.18 31 28.00 12.00 57.65 2.00 0.35 10.31 55.91 9.81 54.70 32 23.23 13.56 55.79 7.12 0.305 7.34 29.80 6.99 30.37 33 28.00 12.00 57.83 2.00 0.17 8.87 43.96 9.17 42.33 34 26.23 17.56 50.21 5.79 0.215 5.98 24.62 5.62 28.73 35 23.23 14.89 55.79 5.79 0.305 5.63 25.05 6.15 28.45 36 26.23 13.56 54.12 5.79 0.305 7.77 38.37 7.96 39.10 37 28.00 20.00 49.65 2.00 0.35 0.00 0.00 1.79 3.86 38 24.65 12.00 61.00 2.00 0.35 7.48 41.25 8.69 41.14 39 22.00 20.00 55.83 2.00 0.17 0.00 0.00 0.96 0.70 FS: flexural strength; UCS: unconfined compressive strength; pred flex: predicted values for flexural strength; pred UCS: predicted values for UCS. 4.1. Compressive Strength The analysis of variance for the UCS predicted model is shown in Table5. Based on the analysis, a change in the amounts of components (cement, water, sand, silt, additives) significantly (p < 0.0001) affects the compressive strength of the cement mortars. The t-ratio reported in Table6 indicates the influence of each component on the resulting compressive strength values. Compared to other main components, the sand has the highest effect on UCS followed by cement and silt. Thus, the maximized value for the responses will be obtained by maximizing the sand component in the mixture. On the other hand, an increase in water amount could decrease the UCS value which is shown as a negative ratio of −1.63 in Table6. The water and additive did not show a significant effect on the compressive strength (p 6≤ 0.05). However, despite their insignificance, the water and additive were not deleted from the model. It must be noted that components/ingredients could not be deleted in a mixture design process since the resulting mixture will be different than the initial one. For instance, if cement is removed from the mix, the resulting product will no longer be a cement mortar. It is also noteworthy to mention that the main components in Table6 and Table 8 were coded as pseudo-components. This approach simplifies design construction and model fitting and makes parameter estimates more meaningful. The relationship between silt, sand, cement, water and additives with compressive strength are shown in Figure8. The highest value for compressive strength is only achieved when the silt amount is minimum (2%). However, the addition of 20% silt and 22% cement Materials 2021, 14, 528 9 of 16

could produce mortars with approximately 20 MPa compressive strength (Figure8a). The silt and sand showed a negative correlation, where a decrease in silt content increased the amount of sand in the mixture. In addition, an increase in sand content led to an increase in compressive strength (Figure8b). The effect of sand and cement on the compressive strength is also shown in the ternary plots (Figure9a), where an increase in sand and cement content increases the final compressive strength. The highest UCS values were observed when the sand ratio was between 56 to 58% of the total weight of the mortar. The interaction between silt and the additive was not as significant as the interactions between silt and other components. For instance, based on Figure8c, in a few regions, the decrease or increase of additives did not dramatically affect the overall UCS. An increase in water content (12 to 20%) led to a decrease in the compressive strength of the samples (Figure8d). This was also confirmed in Figure9b and is a well-known phenomenon since an increase in water content reduces the compaction rate of the cement/concrete mixtures leading to lower compressive strength.

Table 5. Analysis of variance for the UCS model.

Source DF Sum of Squares Mean Square F Ratio Model 15 4732.2927 315.486 32.6991 Error 23 221.9074 9.648 Prob > F U. Total 38 4954.2001 - <0.0001 *

Table 6. Effect summary/parameter estimates for compressive strength values.

Term Estimate Std Error t Ratio Prob > |t| (Cement-0.22)/0.2083 85.702806 17.88592 4.79 <0.0001 * (Water-0.12)/0.2083 −62.6514 38.50179 −1.63 0.1173 (Sand-0.43)/0.2083 48.611973 4.193305 11.59 <0.0001 * (Silt-0.02)/0.2083 9.0094789 3.948727 2.28 0.0321 * (Additive-0.0017)/0.2083 −282.3226 278.2153 −1.01 0.3208 Cement * Water −11.66381 88.82965 −0.13 0.8967 Water * Sand −21.87772 67.99387 −0.32 0.7505 Water * Silt 169.97529 66.1488 2.57 0.0171 * Sand * Silt 0.2183764 21.45658 0.01 0.9920 Cement * Additive 5901.5652 1620.775 3.64 0.0014 * Water*Additive 808.67954 1254.483 0.64 0.5255 Cement * Sand * 195.20254 62.03723 3.15 0.0045 * (Cement-Sand) Cement * Silt * 254.78546 63.84024 3.99 0.0006 * (Cement-Silt) Water * Sand * Silt 409.27446 187.3776 2.18 0.0394 * Sand * Silt * (Sand-Silt) −77.41989 18.82222 −4.11 0.0004 * Cement * Water * Additive −23,724.89 6424.449 −3.69 0.0012 * Statistically signiﬁcant parameters are indicated by *. Materials 2021, 14, x FOR PEER REVIEW 10 of 17

response. Moreover, the optimization could be achieved by defining a certain range or limits. During the optimization process, it is also possible to select the best mixture based on a desirable dependent variable. For instance, the profiler option in JMP software was used and the amount of silt was set to 20%. As illustrated in Figure 10, a compressive strength of 22 MPa could be achieved when the mixture contains 22% cement, 13% water, 44 sand, 0.17% additive and 20% of silt. Decreasing the amount of silt to 16% could increase the compressive strength to 26 MPa. However, since the focus of the study was to maximize the use of waste silt, the model was optimized based on the highest amount of silt in the mixture. The real mean values obtained for a different amount of silt is depicted Materials 2021, 14, 528 in Figure 11, where the maximum strength was obtained when only 5% of waste silt10 was of 16 introduced into the mixture.

Figure 8. Contour plots for 28 days compressive strength: ( a) silt–cement; ( b) silt–sand; (c) silt–admixture; (d) silt–water.

One of the aims of conducting a DOE is to benefit from its optimization capabilities. During an optimization process, one could aim for maximizing or minimizing a certain response. Moreover, the optimization could be achieved by defining a certain range or limits. During the optimization process, it is also possible to select the best mixture based on a desirable dependent variable. For instance, the profiler option in JMP software was used and the amount of silt was set to 20%. As illustrated in Figure 10, a compressive strength of 22 MPa could be achieved when the mixture contains 22% cement, 13% water, 44 sand, 0.17% additive and 20% of silt. Decreasing the amount of silt to 16% could increase the compressive strength to 26 MPa. However, since the focus of the study was to maximize the use of waste silt, the model was optimized based on the highest amount of silt in the mixture. The real mean values obtained for a different amount of silt is depicted in Figure 11, where the maximum strength was obtained when only 5% of waste silt was introduced into the mixture.

4.2. Flexural Strength Based on the Anova analysis, a change in the components including cement, water, sand, silt and additive significantly affected the flexural strength of the cement mortars (p < 0.0001) (Table7). The t-ratio reported in Table8 indicates the influence of each component on the resulting flexural strength values. Compared to other main components, the sand has the highest effect on the mechanical properties, followed by cement. Water had a high and negative correlation with flexural strength. Silt and additive do not have a significant effect on the final response. However, the interaction between water-silt is high and comparable to the effect of sand. Thus, the maximized value for the response will be obtained by maximizing the sand component in the mixture. As mentioned before, despite their insignificance, the silt and additive could not be deleted from the model because of the nature of the mixture design. Materials 2021, 14, x FOR PEER REVIEW 11 of 17 Materials 2021, 14, 528 11 of 16

Figure 9. Ternary contour plots forFigure 28 days 9. Ternarycompressive contour strength plots comparing for 28 days component compressive ratios: strength (a) cement–silt–sand; comparing component (b) cement– ratios: silt–water. (a) cement–silt–sand; (b) cement–silt–water.

Materials 2021, 14, 528 12 of 16 MaterialsMaterials 2021 2021, ,14 14, ,x x FOR FOR PEER PEER REVIEW REVIEW 1212 of of 17 17

FigureFigure 10. 10. Optimization Optimization of of 28 28 days days compressive compressive strength strengthstrength based basedbased on onon maximized maximizedmaximized silt siltsilt content. content.content.

FigureFigure 11. 11. Mean Mean values values for for 28 28 days days UCS UCS (M (MPa)(MPa)Pa) versus versus silt silt content content (%). (%).

4.2.Table4.2. Flexural Flexural 7. Analysis Strength Strength of variance for flexural strength model. BasedBased on on the the Anova Anova analysis, analysis, a a change change in in the the components components including including cement, cement, water, water, Source DF Sum of Squares Mean Square F Ratio sand,sand, silt silt and and additive additive significantly significantly affected affected th thee flexural flexural strength strength of of the the cement cement mortars mortars ( (pp << 0.0001) 0.0001)Model (Table (Table 7). 7). The The t-ratio t-ratio 10 reported reported 182.61511in in Ta Tableble 8 8 indicates indicates 18.2615the the influence influence of of each each 28.8415 compo- compo- nentnent on onError the the resulting resulting flexural flexural 28 strength strength values. 17.72870values. Compared Compared to to 0.6332other other main main components, components, Prob > F the the U. Total 38 200.34381 - <0.0001 * sandsand has has the the highest highest effect effect on on the the mechanic mechanicalal properties, properties, followed followed by by cement. cement. Water Water had had aa highhigh andand negativenegative correlationcorrelation withwith flexuralflexural strength.strength. SiltSilt andand additiveadditive dodo notnot havehave aa significantTablesignificant 8. Effect effect effect summary/parameter on on the the final final response. response. estimates However, However, for flexural the the interaction strengthinteraction values. between between water-silt water-silt is is high high andand comparable comparableTerm to to the the effect effect of of sand. Estimatesand. Thus, Thus, the the Stdmaximized maximized Error value value t Ratio for for the the response response Prob >will will |t| be be obtainedobtained by by maximizing maximizing the the sand sand component component in in the the mixture. mixture. As As mentioned mentioned before, before, despite despite (Cement-0.22)/0.2083 17.270749 1.904416 9.07 <0.0001 * theirtheir insignificance, insignificance, the the silt silt and and additive additive co coulduld not not be be deleted deleted from from the the model model because because of of (Water-0.12)/0.2083 −13.02035 1.903995 −6.84 <0.0001 * thethe nature nature(Sand-0.43)/0.2083 of of the the mixture mixture design. design. 9.6833449 0.748193 12.94 <0.0001 * (Silt-0.02)/0.2083 0.5155091 0.79756 0.65 0.5233 TableTable(Additive-0.0017)/0.2083 7. 7. Analysis Analysis of of variance variance for for flexural flexural 73.118157 strength strength model. model. 45.426 1.61 0.1187 Water * Silt 44.859904 4.34842 10.32 <0.0001 * Source DF Sum of Squares Mean Square F Ratio SourceSand * Silt DF Sum 7.619343 of Squares 2.810101 Mean Square 2.71 F 0.0113 Ratio * ModelModelCement * Sand1010 * 182.61511 182.61511 18.2615 18.2615 28.8415 28.8415 31.001789 10.3192 3.00 0.0056 * ErrorError(Cement-Sand) 28 28 17.72870 17.72870 0.6332 0.6332 Prob Prob > > F F U.U. Total TotalCement * Silt 38 38 * 200.34381 200.34381 - - <0.0001 <0.0001 * * 23.954223 10.01144 2.39 0.0237 * (Cement-Silt) TableTableSand 8. 8. Effect Effect * Silt summary/parameter *summary/parameter (Sand-Silt) − estimate 15.11265estimatess for for flexural flexural 4.698824 strength strength values. values.−3.22 0.0033 * Water * Additive * −1462.162 549.0786 −2.66 0.0127 * (Water-Additive)TermTerm EstimateEstimate StdStd Erro Errorr t tRatio Ratio Prob Prob > > |t| |t| Statistically(Cement-0.22)/0.2083(Cement-0.22)/0.2083 significant parameters are indicated by17.270749 *.17.270749 1.9044161.904416 9.079.07 <0.0001<0.0001 * * (Water-0.12)/0.2083(Water-0.12)/0.2083 −−13.0203513.02035 1.9039951.903995 −−6.846.84 <0.0001<0.0001 * *

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(Sand-0.43)/0.2083 9.6833449 0.748193 12.94 <0.0001 * The relationship between silt, sand, cement, water and additive with the flexural (Silt-0.02)/0.2083 0.5155091 0.79756 0.65 0.5233 strength(Additive-0.0017)/0.2083 are shown in Figure 12 . Like the compressive73.118157 strength,45.426 the highest1.61 value0.1187 for flexural strength isWater only achieved* Silt when the silt 44.859904 amount is set to 4.34842 a minimum 10.32 (2%). However, <0.0001 * the addition of 20%Sand silt * andSilt 22% cement could 7.619343 produce mortars 2.810101 withstanding 2.71 approximately 0.0113 * 4.0 MPaCement of flexural* Sand * (Cement-Sa strength (Figurend) 12a).31.001789 With the silt content10.3192 fixed3.00 at 2%, the0.0056 flexural * strengthCement decreased * Silt * (Cement-Silt) when the sand content 23.954223 increased from 10.01144 50 to 55%. 2.39 This could 0.0237 be * due to the higherSand * content Silt * (Sand-Silt) of cement in the mixture−15.11265 (Figure 124.698824b). However, −3.22 when the0.0033 amount * ofWater silt is * increased Additive * to(Water-Additive) 10% or higher, an increase−1462.162 of sand549.0786 content in the−2.66 mortar 0.0127 mixtures * Statisticallyimproves thesignificant flexural parameters strength. are An indicated increase by in *. the additive content improved the flexural strength, which is visible in the contour plot (Figure 12c). Regardless of silt content, the flexuralThe valuesrelationship improve between with higher silt, sand, additive cement, values. water However, and additive this trend with reverses the flexural and the strengthflexural strengthare shown starts in Figure to decrease 12. Like for additivethe compressive values higher strength, than the 0.3%. highest Similar value trends for wereflex- uralobservable strength for is theonly model achieved constructed when the for silt the amount compressive is set to strength. a minimum The (2%). effects However, of water, thesand addition and silt of on 20% the silt flexural and 22% strength cement are could also observable produce mortars in the ternarywithstanding plot (Figure approxi- 13). matelyThe increase 4.0 MPa of siltof followsflexural astrength similar trend(Figure to water12a). With and decreasesthe silt content the flexural fixed strengthat 2%, the of flexuralthe mixture. strength However, decreased it is onlywhen the the sand sand that content improves increased the final from strength 50 to 55%. of the This mixture. could be dueThe to profilerthe higher option content in JMP of cement software in wasthe usedmixture and (Figure the silt 12b). was setHowever, to 20 percent. when the As amountillustrated of insilt Figure is increased 14, a flexural to 10% strength or higher, of 4.0 an MPa increase could of be sand achieved content when in thethe mixturemortar mixturescontains 22%improves cement, the 13%flexural water, strength. 44.83% An sand, increase 0.17% in additive the additive and 20% content of silt. improved Decreasing the flexuralthe amount strength, of silt which to 16% is could visible increase in the contour the strength plot (Figure to 6 MPa. 12c). However, Regardless as highlighted of silt content,before, the the flexural current values work improve aimed to with improve higher the additive silt content values. without However, compromising this trend there- versescement and mortar the performances.flexural strength Water starts to cementto decrease ratios for for additive both responses values washigher approximately than 0.3%. W C Similar0.59 which trends is slightlywere observable higher than for thethe normalmodel constructed values ( / for= the 0.5). compressive Due to the naturestrength. of silt/clay particles extra water is required to increase the workability of the cement mortar The effects of water, sand and silt on the flexural strength are also observable in the ter- mixtures [29]. The real mean values obtained for different amounts of silt is depicted in nary plot (Figure 13). The increase of silt follows a similar trend to water and decreases Figure 15, where the maximum strength was obtained when only 5% of waste silt was the flexural strength of the mixture. However, it is only the sand that improves the final introduced into the mixture. The overall trend indicates and confirms a decrease in the strength of the mixture. strength with an increase of silt content in the mortar mixtures.

FigureFigure 12. 12. ContourContour plots plots for for 28 28 days days flexural ﬂexural strength: strength: ( (aa)) silt–cement; silt–cement; ( (bb)) silt–sand; silt–sand; ( (cc)) silt–admixture; silt–admixture; ( (d)) silt–water. silt–water.

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Materials 2021, 14, 528 14 of 16 Materials 2021, 14, x FOR PEER REVIEW 14 of 17

Figure 13. Ternary contour plot for 28 days flexural strength.

The profiler option in JMP software was used and the silt was set to 20 percent. As illustrated in Figure 14, a flexural strength of 4.0 MPa could be achieved when the mixture contains 22% cement, 13% water, 44.83% sand, 0.17% additive and 20% of silt. Decreasing the amount of silt to 16% could increase the strength to 6 MPa. However, as highlighted before, the current work aimed to improve the silt content without compromising the cement mortar performances. Water to cement ratios for both responses was approximately 0.59 which is slightly higher than the normal values (W/C = 0.5). Due to the nature of silt/clay particles extra water is required to increase the workability of the cement mortar mixtures [29]. The real mean values obtained for different amounts of silt is depicted in Figure 15, where the maximum strength was obtained when only 5% of waste silt was introduced into the mixture. The overall trend indicates and confirms a decrease in the strength with an increase of silt content in the mortar mixtures. Figure 13.FigureTernary 13. Ternary contour contour plot plot for for 28 28 daysdays flexural ﬂexural strength. strength. The profiler option in JMP software was used and the silt was set to 20 percent. As illustrated in Figure 14, a flexural strength of 4.0 MPa could be achieved when the mixture contains 22% cement, 13% water, 44.83% sand, 0.17% additive and 20% of silt. Decreasing the amount of silt to 16% could increase the strength to 6 MPa. However, as highlighted before, the current work aimed to improve the silt content without compromising the cement mortar performances. Water to cement ratios for both responses was approximately 0.59 which is slightly higher than the normal values (W/C = 0.5). Due to the nature of silt/clay particles extra water is required to increase the workability of the cement mortar mixtures [29]. The real mean values obtained for different amounts of silt is depicted in Figure 15, where the maximum strength was obtained when only 5% of waste silt was introduced into the mixture. The overall trend indicates and confirms a decrease in the strength with an increase of silt content in the mortar mixtures.

Materials 2021, 14, x FOR PEER REVIEW 15 of 17

FigureFigure 14. Optimization 14. Optimization of of 28 28 days days ﬂexural flexural strengthstrength (MPa) based based on on maximized maximized silt siltcontent. content.

Figure 14. Optimization of 28 days flexural strength (MPa) based on maximized silt content.

Figure 15. 15. MeanMean values values for 28 fordays 28 flexural days streng ﬂexuralth (FS) strength (MPa) versus (FS) silt (MPa) content versus (%). silt content (%).

5. Conclusions The current research focused on producing cement mortars by partially substituting natural sand with the waste silt obtained from the limestone aggregate production in S.A.P.A.B.A. s.r.l. (Italy). Thus, a DOE method was proposed to define the optimum mix design, aiming to include waste silt without affecting the final performance of the cement mortar. Three cement mortar beams were produced and tested for each of the 49 randomized mixtures defined by the DOE method. The obtained results validate the design approach and suggest the possibility of substituting up to 20% of natural sand with waste silt in cement mortar mixtures. The corresponding maximum compressive and flexural strength were reported as 22 MPa and 4.0 MPa, respectively. However, by reducing the silt amount to 16%, the compressive and flexural strength will increase to approximately 30 and 6.5 MPa, respectively. The silt particles are larger than natural sand and shorter than clay minerals. When silt and clay particles are introduced into cement/concrete mixtures, more water is required for producing a homogenized mixture. However, the addition of too much water could decrease the compaction of the mixture and reduce the final strength of the mixture. The addition of a special admixture improved the mixing process of the mixture even though it showed no significant effects on the models. Recycling a high amount of washed silt into cement-bound materials could dramatically help with the recycling process of such material and decrease its adverse impact on the environment.

Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1, Equation 1S: Predicted model for FS, Equation 2S: Predicted model for UCS . Author Contributions: conceptualization, A.S., P.T. and C.S.; data curation, A.S.; formal analysis, A.S.; investigation, A.S.; methodology, A.S.; project administration, A.S.; spervision, G.V., P.T. and C.S.; validation, P.T.; writing—original draft, A.S.; writing—review and editing, G.V., P.T. and C.S. All authors have read and agreed to the published version of the manuscript. Funding: This paper has been written for the SAFERUP! Project, which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska- Curie grant agreement No 765057.

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5. Conclusions The current research focused on producing cement mortars by partially substituting natural sand with the waste silt obtained from the limestone aggregate production in S.A.P.A.B.A. s.r.l. (Italy). Thus, a DOE method was proposed to define the optimum mix design, aiming to include waste silt without affecting the final performance of the cement mortar. Three cement mortar beams were produced and tested for each of the 49 randomized mixtures defined by the DOE method. The obtained results validate the design approach and suggest the possibility of substituting up to 20% of natural sand with waste silt in cement mortar mixtures. The corresponding maximum compressive and flexural strength were reported as 22 MPa and 4.0 MPa, respectively. However, by reducing the silt amount to 16%, the compressive and flexural strength will increase to approximately 30 and 6.5 MPa, respectively. The silt particles are larger than natural sand and shorter than clay minerals. When silt and clay particles are introduced into cement/concrete mixtures, more water is required for producing a homogenized mixture. However, the addition of too much water could decrease the compaction of the mixture and reduce the final strength of the mixture. The addition of a special admixture improved the mixing process of the mixture even though it showed no significant effects on the models. Recycling a high amount of washed silt into cement-bound materials could dramatically help with the recycling process of such material and decrease its adverse impact on the environment.

Supplementary Materials: The following are available online at https://www.mdpi.com/1996-194 4/14/3/528/s1, Equation 1S: Predicted model for FS, Equation 2S: Predicted model for UCS. Author Contributions: Conceptualization, A.S., P.T. and C.S.; data curation, A.S.; formal analysis, A.S.; investigation, A.S.; methodology, A.S.; project administration, A.S.; spervision, G.V., P.T. and C.S.; validation, P.T.; writing—original draft, A.S.; writing—review and editing, G.V., P.T. and C.S. All authors have read and agreed to the published version of the manuscript. Funding: This paper has been written for the SAFERUP! Project, which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 765057. Institutional Review Board Statement: Not Applicable. Informed Consent Statement: Not Applicable. Data Availability Statement: All data has been provided within the article. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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